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Review of Radar Remote Sensing on Urban Areas
Abstract
Synthetic Aperture Radar (SAR) is an active remote sensing technique capable of providing high-resolution imagery independent
from daytime and to great extent unimpaired by weather conditions. However, SAR inevitably requires an oblique scene illumination
resulting in undesired occlusion and layover especially in urban areas. As a consequence, SAR is without any doubt not the
first choice for providing complete coverage of urban areas. For such purpose, sensors being capable of acquiring high-resolution
data in nadir view are better suited like optical cameras or airborne laserscanning devices. Nevertheless, there are at least
two kinds of application scenarios concerning city monitoring where the advantages of SAR play a key role: firstly, time critical
events and, secondly, the necessity to gather gap-less and regular spaced time series of imagery of a scene of interest.
... Longer wavelengths are also better able to penetrate ground surfaces, vegetation canopies, and especially cloud cover. X-band is considered the lower limit to ensure all weather mapping capability (Xia & Henderson, 1997;Soergel, 2010). ...
... Different scattering mechanisms are observable when the radiation interacts with different surfaces. Single bounce scattering occurs off roofs, which can result in a strong signal response (Dong et al, 1997;Soergel, 2010). This is also observed when streets are oriented in the along-track direction. ...
... This is also observed when streets are oriented in the along-track direction. Smooth horizontal surfaces such as asphalt roads, parking lots, and calm water are also single bounce scatterers but they appear dark because most of the signal is reflected away from the sensor (Soergel, 2010). ...
Highly accurate classifications of urban Land Use/Land Cover (LULC) are critical for many remote sensing applications in urban geography. A review of the literature shows that there is no one method that consistently achieves this. Therefore, the goal of this study is to investigate the ability of out-of-box, pixel-based, medium-resolution image fusion classifications to increase the thematic classification accuracy of urban LULC mapping. The objectives identified to achieve this goal were to (1) establish a baseline of nonfusion classification results using supervised and unsupervised algorithms and (2) test 2 fusion methods to explore whether they would improve classification results. The unsupervised optical classification and the image fusion using radar products (H/A/α) with optical and near-infrared (NIR) bands both achieved excellent overall accuracies (89.4% and 89.1%, respectively). Lower accuracies were obtained using a principal components analysis of both optical and radar data. Overall, optical-radar image fusion improved urban LULC classification. The choice of how LULC classes are aggregated and which algorithm is chosen were found to be critical in achieving adequate results for urban mapping.
... Different portions of the electromagnetic spectrum are useful in analyzing urban environments from the reflective spectral range to the microwave radar [14]. The latter provide highresolution images independent of the time of day and weather; however, due to the requirement of oblique illumination of the scene, occlusion and layover appear, making the analysis of dynamic urban areas difficult [15]. ...
... In recent years, a multiwavelength lidar scanner has become an interesting mapping device that can differentiate objects with the same height, such as buildings and trees, based on pseudonormalized difference vegetation index (pseudoNDVI) [68] and geometrical features, e.g., roughness (curvature) [120]. Teo and Wu [15] provided a case study where curvature, intensity, and nDSM were used on multispectral lidar. They applied these lidar features as input for image clustering and found that especially geometric features are suitable for building detection. ...
Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.
... Radiometers satellites represent passive radar sensors, and their data helps assess the vapor content of the atmosphere. While the sensors of active radar can also be divided into the sensors of non-imaging such as radar altimeters and scatterometers, and imaging sensors such as earth exploration satellites (Soergel, 2010). The radar system is based on the truth that the matter interacts with electromagnetic waves and part of the transferred energy is intercepted by a potential object and re-radiation in several directions. ...
... The lowest distance separating two targets on the surface of the earth, which can be distinguished along the ground range direction is called ground range resolution ( Fig.2.3) (Richards, 2009) while the slant range resolution is the resolution of two targets along slant direction or along the Line Of Sight (LOS) direction of the radar. Ground range resolution is a task of incidence angle while the slant range resolution is a task of the length of the pulse (Woodhouse, 2017), which is inverse proportional to the pulse signal bandwidth (Soergel, 2010). So, the difference between the resolutions of the ground range and slant range is only in the sin ( Fig.2.3) which is that angle created by the incoming beam with the normal position of the surface and represented the projection of the pulse on the surface (Elachi & Van Zyl, 2006). ...
... i.a. Soergel, 2010). Crop mapping at a specific time or growth stage is of high importance for agricultural and economic applications. ...
... First, the classification was applied to two TSX scenes using each SAR image as a single image then both of the two available images were used jointly. The advantage of the analysis of single SAR images (besides their costs) is the necessity of rapid mapping, for instance in the case of time critical events (Soergel, 2010). In the present study, field boundaries were digitised using the digital ortho-photos (DOPs) with 20 cm spatial resolution. ...
The present study aims to evaluate the field-based approach for the
classification of land cover using the recently launched high resolution
SAR data. TerraSAR-X1 (TSX-1) strip mode im-age, coupled with Digital
Ortho Photos with 20 cm spatial resolution was used for land cover
classification and parcel mapping respectively. Different filtering and
texture analyses tech-niques were applied to extract textural
information from the TSX-1 image in order to assess the enhancement of
the classification accuracy. Several attributes of parcels were derived
from the available TSX-1 image in order to define the most suitable
attributes discriminating be-tween different land cover types. Then,
these attributes were further analyzed by statistical and various image
classification methods for landcover classification. The results showed
that, tex-tural analysis performed higher classification accuracy than
the earlier. The authors conclude that, an integrated landcover
classification using the textural information in TerraSAR-X1 has high
potential for landcover mapping. Key words: Landcover classification,
TerraSARX1, field based, texture analysis
... i.a. Soergel, 2010). Crop mapping at a specific time or growth stage is of high importance for agricultural and economic applications. ...
... First, the classification was applied to two TSX scenes using each SAR image as a single image then both of the two available images were used jointly. The advantage of the analysis of single SAR images (besides their costs) is the necessity of rapid mapping, for instance in the case of time critical events (Soergel, 2010). In the present study, field boundaries were digitised using the digital ortho-photos (DOPs) with 20 cm spatial resolution. ...
The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20 cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping.
... In comparison to the same data for the USA, fewer rectangular shapes of roads and urban layout exist, which hampers urban feature identification. Remote sensing offers a range of further options for multi-spectral data or data in high spatial resolution, such as usage of thermal sensors (Voogt and Oke 2003), night-time light urban feature extraction (Wentz et al. 2014;Zhao et al. 2019), object-based classification (Blaschke 2010;Kucharczyk et al. 2020), LiDAR data (Yan et al. 2015Wedajo 2017), or radar data (Soergel 2010). This is promising for recent satellite data but has limitations when applied to greyscale older satellite images with often medium or rather coarse spatial resolution. ...
Kenya experiences massive urban growth, also into natural hazard-prone areas, exposing settlements and the natural environment to riverine and pluvial floods and other natural hazards. While Nairobi as the capital and principal city has been extensively analysed regarding urban growth and flood hazard in some central parts, awareness of growing peri-urban areas has not been studied as much. The results are of interest to other locations in Kenya and worldwide, too, since the current research and disaster risk practice focus is still too much on megacities and city centres. Therefore, the study compares urban growth into hazard areas in urban rims of Nairobi and Nyeri, Kenya. A change assessment from 1948 to 2020 is conducted by aerial images, declassified satellite images, and recent data. Urban growth rates are 10- to 26-fold, while growth into flood exposed areas ranges from 2- to 100-fold. This study reveals unused opportunities for expanding existing land-use change analysis back to the 1940s in data-scarce environments.
... But of course, the manual mapping is also constrained by original data resolution and mapping skills. (Soergel 2010). This is promising for recent satellite data but has limitations when applied to greyscale older satellite images with often medium or rather coarse spatial resolution. ...
Kenya experiences massive urban growth, also into natural hazard-prone areas, exposing settlements and the natural environment to riverine and pluvial floods and other natural hazards. While Nairobi as the capital and principal city has been extensively analysed regarding urban growth and flood hazard in some central parts, awareness of growing peri-urban areas has not been studied as much. The results are of interest to other locations in Kenya and worldwide, too, since the current research and disaster risk practice focus is still too much on megacities and city centres. Therefore, the study compares urban growth into hazard areas in urban rims of Nairobi and Nyeri, Kenya. A change assessment from 1948 to 2020 is conducted by aerial images, declassified satellite images, and recent data. Urban growth rates are 10 to 20-fold, while growth into flood exposed areas ranges from 3 to 100-fold. This study reveals unused opportunities for expanding existing land-use change analysis back to the 1940s in data-scarce environments.
... Object-based classification often targets building extraction and contributes to urban object-based image analysis [77,78]. Another emerging field is radar satellite data applied for urban studies [79]. LiDAR data is also increasingly used for both urban and natural hazard assessments [80,81]. ...
Remote sensing applications of change detection are increasingly in demand for many areas of land use and urbanization, and disaster risk reduction. The Sendai Framework for Disaster Risk Reduction and the New Urban Agenda by the United Nations call for risk monitoring. This study maps and assesses the urban area changes of 23 Mexican-USA border cities with a remote sensing-based approach. A literature study on existing studies on hazard mapping and social vulnerability in those cities reveals a need for further studies on urban growth. Using a multi-modal combination of aerial, declassified (CORONA, GAMBIT, HEXAGON programs), and recent (Sentinel-2) satellite imagery, this study expands existing land cover change assessments by capturing urban growth back to the 1940s. A Geographic Information System and census data assessment results reveal that massive urban growth has occurred on both sides of the national border. On the Mexican side, population and area growth exceeds the US cities in many cases. In addition, flood hazard exposure has grown along with growing city sizes, despite structural river training. These findings indicate a need for more risk monitoring that includes remote sensing data. It has socioeconomic implications, too, as the social vulnerability on Mexican and US sides differ. This study calls for the maintenance and expansion of open data repositories to enable such transboundary risk comparisons. Common vulnerability variable sets could be helpful to enable better comparisons as well as comparable flood zonation mapping techniques. To enable risk monitoring, basic data such as urban boundaries should be mapped per decade and provided on open data platforms in GIS formats and not just in map viewers.
... Radar imaging technologies are one of the important technologies in the field of RS (Bui et al. 2021;Yu et al. 2019). Using Radar brings new perspectives and broadens researchers' understanding, particularly in managing natural hazards (Soergel 2010;Kääb et al. 2005;Hajeb et al. 2020;Koohbanani et al. 2020). Interferometric Synthetic Aperture Radar (InSAR) is a well-established technique that monitors large areas according to classical ground-based measurement methods. ...
İzmir, the third-largest city and economic center of Turkey, has been exposed to many natural disasters throughout its history. It is one of them in the 6.6 magnitude earthquake that last occurred on October 30, 2020. In this study, the PSInSAR technique was applied on 26 descending Sentinel-1 images between February 2020 and January 2021 to monitor the land change of the study area determined in Izmir. PSInSAR method was carried out with the SNAP-StaMPS integration, whose methodology is given below. The annual velocity values of the PS points produced in the study area in the direction of satellite view were found between – 68 and 32 mm/year. As a result of the study, it was observed that the most prominent surface change occurred in Çiğli and Karşıyaka districts. The points containing the speed information obtained from the PSInSAR result, PS points, and maps showing changes in the land were shared online to be used as auxiliary data in other studies.
... Se conoce para t1 y t2 la distancia entre el satélite y el objeto en la superficie de la Tierra (R1 y R2 respectivamente). A partir de la diferencia ∆R (siendo esta R1 -R2) se puede obtener alturas del terreno o determinar deformaciones verticales (Campbell & Wynne, 2011;Kerle, Janssen, & Huurneman, 2004;Soergel, 2010). ...
Resumen El objetivo de esta investigación fue aplicar el método Persistent Scatterer Interferometry (PSI) en la ciudad de Limón, Costa Rica, con el fin de estimar la velocidad de deformación de la superficie. La investigación es de tipo descriptiva y el enfoque que se utilizó es el uso de imágenes de Radar de la misión Sentinel-1, las cuales fueron preprocesadas en el programa SNAP de la Agencia Espacial Europea y luego usando el programa StaMPS se estimó la velocidad en la línea de vista (LOS) y series de tiempo. Como resultado se tiene que las velocidades estimadas en LOS están en el rango de-11 mm/yr hasta +20 mm/yr. Se concluye que el método tiene un potencial para ser usado en
... Object-based image analysis (OBIA) has already been applied in urban areas using VHR radar imagery, but primarily for change detection based on multi-temporal approaches and not identifying dwellings from single images (Pirrone et al., 2020). One key to successfully identifying dwellings is the understanding and exploitation of typical patterns of double-bounce and signal shadow caused by solid buildings and how these relate to the actual footprint (Soergel, 2010). Once more, this is complicated by partial lacking of backscatter from buildings containing natural construction materials. ...
While many studies exist to identify buildings from optical satellite images, radar-based approaches are still lacking in humanitarian contexts. This article outlines the main challenges related to scattering mechanisms returning from huts, tents, informal dwellings, and their natural surroundings, but also from geometric distortions caused by the side-looking radar aperture. An outlook summarizes how these limitations can be overcome by image enhancement or multi-image composites, but also by advanced methods on building extraction, such as convolutional neural networks (CNNs). This article aims to stimulate scientific debate and to lay a foundation for the development of new methods.
... InSAR uyduları ile gerçekleştirilen Sayısal Yüzey Modeli (SYM) üretiminde, uçuş yönüne dik (enine) uygun uzunlukta bir baz mesafesinde iki SAR görüntüsü alınır. Topoğrafik yükseklik bilgisi, yöneltilmiş bu iki SAR görüntüsü arasındaki faz farkından elde edilir (Soergel, 2010;Soergel vd., 2013). Uydular ile elde edilen SAR verisi genellikle tekrarlı geçiş tekniği ile toplanır. ...
TerraSAR-X and TanDEM-X satellites are designed by German Aerospace Center (Deutsches Zentrum
für Luft und Raumfahrt, DLR) and were launched in 2007 and 2010 respectively. The two satellites are
orbiting each other at a distance of 250-500 m, providing simultaneous interferometry and producing
high accuracy and resolution Digital Elevation Models. It is expressed by DLR that The Digital Surface
Model called WorldDEM obtained by TanDEM-X satellite group; has a spatial resolution of
approximately 12 m (0.4 arc seconds), absolute accuracy better than 10 m (LE90), relative accuracy
better than 2 m at slopes less than 20% and 4 m in other areas. The accuracy of WorldDEM, which is
initially produced by a single pass interferometry is improved by processing further passes of the
satellites. In this study, the WorldDEM data generated from the single and double passes of the
satellites were tested using geodetic points located in the region and 5 m resolution Digital Surface
Models (HGK SYM) produced by the General Command of Mapping beginning from 2013 from stereo
aerial photographs with automatic image matching techniques. The results show that the DEM
produced from double pass has higher accuracy both relatively and absolutely, especially errors in the
mountainous areas are decreased by double pass and the slope has a negative effect on the accuracy.
Also comparisons with geodetic points showed that the accuracy of 1.48 meter from the single pass
increases to 1.27 meter by the double pass.
... Cheruto et al. [9] assessed LULC changes using geographic information systems (GIS) and remote sensing techniques in Makueni County, Kenya. The LULC classification is perhaps among the most well-known applications of geospatial application [28,30]. ...
Anthropogenic activities have substantially changed natural landscapes, especially in regions which are extremely affected by population growth and climate change such as East African countries. Understanding the patterns of land-use and land-cover (LULC) change is important for efficient environmental management, including effective water management practice. Using remote sensing techniques and geographic information systems (GIS), this study focused on changes in LULC patterns of the upstream and downstream Wami River Basin over 16 years. Multitemporal satellite imagery of the Landsat series was used to map LULC changes and was divided into three stages (2000–2006, 2006–2011, and 2011–2016). The results for the change-detection analysis and the change matrix table from 2000 to 2016 show the extent of LULC changes occurring in different LULC classes, while most of the grassland, bushland, and woodland were intensively changed to cultivated land both upstream and downstream. These changes indicate that the increase of cultivated land was the result of population growth, especially downstream, while the primary socioeconomic activity remains agriculture both upstream and downstream. In general, net gain and net loss were observed downstream, which indicate that it was more affected compared to upstream. Hence, proper management of the basin, including land use planning, is required to avoid resources-use conflict between upstream and downstream users.
... Como los sistemas de radar tienen su propia fuente de energía, a diferencia de los sensores pasivos no son afectados ni dependen tampoco de las condiciones atmosféricas. Esto permite tener no sólo mayor fiabilidad para la comparación entre dos o más imágenes SAR, sino también, mayor continuidad de la información en intervalos de tiempo definidos (Soergel, 2010). Asimismo, con respecto a otros productos satelitales, especialmente los generados por sensores pasivos, las imágenes de radar presentan unas ventajas significativas para el monitoreo del crecimiento urbano. ...
Dentro del estudio de las áreas urbanas a partir de InSAR siempre han destacado los trabajos que hacen uso sólo de la coherencia para la identificación de las zonas construidas. En los casos en que se busca aprovechar la información derivada de radar en más de una polarización, casi todos los estudios se centran en las cuatro polarizaciones completas. Este trabajo, sin embargo, pretendió ir más allá explorando la potencialidad y las limitaciones de la InSAR incluyendo y analizando no sólo la coherencia interferométrica, sino también las operaciones entre dos polarizaciones cruzadas.
En este sentido, y ante la información existente, se buscó sacar el máximo provecho a losdatos disponibles de los sensores Envisat y Alos-Palsar desarrollando una metodología sencilla, que abarca desde las fases de pre procesamiento de las señales en estado crudo hasta su análisis y validación cuantitativa y cualitativa en las aplicaciones sobre la ciudad.Los resultados obtenidos demuestran
una enorme potencialidad para los datos derivados de InSAR con imágenes de una y dos polarizaciones, puesto que fue posible lograr una buena caracterización de diversas áreas construidas
con distintos grados de urbanización dentro de la Zona Metropolitana del Valle de México, en comparación con una clasificación supervisada. Estos resultados son valiosos igualmente porque demuestran qué aspectos dentro del procesamiento propuesto pueden considerarse como limitaciones
de la InSAR, especialmente en lo que se refiere a la orientación del trazado urbano. En todo caso, una posibilidad sigue abierta en función de los objetivos de esta investigación: se trata de la incorporación de otras polarizaciones donde predomine el plano vertical, como en la VV y la VH, cuya alta correlación con la polarización HV probablemente mejorará aún más los resultados de la
estimación, y asimismo disminuirá los errores asociados a la orientación.
... While block-based UST classification with optical imagery is a well-covered application, synthetic aperture radar (SAR) and interferometric SAR (InSAR) imagery have seldom been used for that purpose, as noticed by Chen et al. (2012) and Walde et al. (2014). The advantages of (In)SAR systems (Woodhouse 2006), their potential for detecting and characterizing buildings (Stilla 2007;Soergel 2010) and the fact that in some parts of the world up-to-date aerial imagery may be lacking are factors that motivate the investigation of the extent to which spaceborne high-resolution InSAR imagery can be used for the automatic classification of the USTs from urban blocks. A decisive aspect in this context is the consideration of expressive block attributes derived from the images. ...
This work presents an approach for classifying urban blocks according to their built-up structure based on high-resolution spaceborne InSAR images. Most attributes considered in the classification describe the geometric structure and spatial disposition of the polygon and line features extracted from each block. The feature extraction is carried out on two intensity images acquired at the satellite’s ascending and descending orbits. The strategy used for extracting polygon features is described in detail. We also present a Markov random field model used to perform context-based classification of built-up structures. The model establishes a probabilistic dependency between the class labels of two neighbouring blocks, taking in this way advantage of the fact that blocks with the same structure are frequently clustered. 1695 urban blocks were classified into five general built-up types. It is shown that the context-based classification accuracy is up to 6% more accurate than the standard classification on which the context-based model is based. We hence provide evidence (1) that urban block-based classifications can potentially be improved if context is considered and (2) that general built-up structures can be distinguished to a good extent using available high-resolution spaceborne radar images.
... processing-While optical sensors are limited by lack of transmission of short to medium wavelength electromagnetic energy through clouds and precipitation, synthetic aperture radar (SAR) sensors are capable of transmitting and receiving microwave energy that is sensitive to physical characteristics of land surfaces such as roughness, morphology and geometry in most atmospheric conditions (Soergel 2010). Applications of SAR imagery for urban and built area mapping have proven to be very effective, given the high return characteristic of man-made features (Haack and Bechdol 2000). ...
In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM +) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 m uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.
... R ADAR images are used for various applications in urban areas [1], e.g., classification purposes, growth monitoring, change detection, or surveillance of sensitive urban areas. To meet with the different needs, it is possible to use the socalled advanced synthetic aperture radar (SAR) modes, such as interferometry [2], [3], to provide an estimate of elevations, or polarimetry, to analyze the type of scattering mechanisms encountered. ...
This letter focuses on the analysis of layover effects in interferometric synthetic aperture radar (SAR) data of urban areas. In particular, we derive two formulations to express the backscattering coefficient and the interferometric coherence in this case. These equations show that the backscattering coefficient and the interferometric coherence in layover areas can be seen as a combination of the backscattering coefficients and interferometric coherences of the individual scattering mechanisms. These formulations are then tested on interferometric SAR (InSAR) data and analyzed statistically.
... DEM generation based on interferometric SAR (InSAR) requires at least two complex SAR images which feature an across-track baseline of suitable length. The topographic height information is derived from the phase difference (i.e., the interferogram) of the coregistered individual SAR images (Soergel, 2010). Space borne SAR data are usually acquired in so-called repeatpass mode, this means that the imagery are taken from the same orbit but separately at different dates, the time lapse is usually an integer multiple of the revisit cycle of the given satellite in use. ...
The German Aerospace Center (DLR, Deutsches Zentrum für Luft- und Raumfahrt) currently conducts the bistatic interferometric
synthetic aperture radar (SAR) Mission TanDEM-X, which shall result in a DEM of global coverage in an unprecedented resolution
and accuracy according to DTED level 3 standard. The mission is based on the two SAR satellites TerraSAR-X and TanDEM-X that
have been launched in June 2007 and 2010, respectively. After the commissioning phase of TanDEM satellite and the orbital
adjustment the bistatic image acquisition in close formation began end of 2010. The data collection for the mission is scheduled to
last about three years, i.e., the bigger part of the required data have been already gathered. Based on this data DLR will conduct
several processing steps in order to come up finally with a global and seamless DEM of the Earth's landmass which shall meet the
envisaged specifications. Since the entire mission is an endeavor in the framework of a private-public-partnership, the private
partner, Astrium, will eventually commercialize the DEM product. In this paper, we will provide an overview of the data collection
and the deliverables that will come along with TanDEM-X mission. Furthermore, we will analyze a DEM derived from early stage
immediate products of the mission.
... The advantage of VHR imagery for cartographic applications is obvious (Soergel et al., 2006). Yet the real potential of this class of SAR data for urban mapping lies in applications where the coherent nature of SAR data is exploited, such as in interferometry or tomography (Soergel, 2010). ...
The topic of this thesis is very high resolution (VHR) tomographic SAR inversion for urban infrastructure monitoring. To this end, SAR tomography and differential SAR tomography are demonstrated using TerraSAR-X spotlight data for providing 3-D and 4-D (spatial-temporal) maps of an entire high rise city area including layover separation and estimation of deformation of the buildings. A compressive sensing based estimator (SL1MMER) tailored to VHR SAR data is developed for tomographic SAR inversion by exploiting the sparsity of the signal. A systematic performance assessment of the algorithm is performed regarding elevation estimation accuracy, super-resolution and robustness. A generalized time warp method is proposed which enables differential SAR tomography to estimate multi-component nonlinear motion. All developed methods are validated with both simulated and extensive processing of large volumes of real data from TerraSAR-X.
Multisensor data fusion demand in Earth observations is constantly increasing thanks to technological advances and the willingness to explore the Earth in a multidisciplinary way. Recently hyperspectral imaging has become a promising tool for Earth monitoring purposes but has also emerged as suitable for fusion with other remote sensors for various applications.
This dissertation examines different types of multisensor data fusion, such as feature-level and application-level fusion, where each application is based on hyperspectral imaging at the airborne scale. In feature-level data fusion, hyperspectral imaging is combined with LiDAR (Light Detection and Ranging) to analyze urban environments, mainly focusing on urban land cover classification and implementing deep learning algorithms. In contrast, application-level data fusion presents the integration of hyperspectral imaging with magnetic data for material characterization of geologic complexes in remote and harsh environments, such as Greenland.
This PhD thesis focused on enhancing analysis outcomes by combining hyperspectral imaging with other sensors and precisely selecting applications in which one sensor is insufficient to obtain the required parameters.
The analysis of feature-level data fusion for hyperspectral and LiDAR data began with a detailed review of sensor key characteristics most representative of urban land cover analysis. These features were intended to segment land cover classes by considering 2D and 3D convolutional operations, where 2D convolutions involve spatial information and 3D convolutions add a spectral dimension allowing the inclusion of information about the interrelation of hyperspectral bands. The study on feature-level data fusion was completed with a multitemporal analysis, where a general framework was proposed towards automatical updating a local urban database.
The other part of the dissertation was based on the fusion of sensors operating in different feature vectors with a common factor: identifying iron and its magnetic properties. Iron in hyperspectral imaging also has distinct absorption features recognizable at the relatively low spatial resolution. Moreover, it is the only chemical element capable of maintaining magnetic properties, which is the main aim of magnetic surveys.
This dissertation has contributed new approaches to various feature-level and application-level multisensor data fusion exploitations confirming the great potential and versatility and showing future directions of multidisciplinary research using remote sensing methods for Earth observation.
Coastal wetlands areas are heterogeneous, highly dynamic areas with complex interactions between terrestrial and marine ecosystems, making them essential for the biosphere and the development of human activities. Remote sensing offers a robust and cost-efficient mean to monitor coastal landscapes. In this paper, we evaluate the potential of using high resolution satellite imagery to classify land cover in a coastal area in Concepción, Chile, using a machine learning (ML) approach. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were evaluated using four different scenarios: (I) using original spectral bands; (II) incorporating spectral indices; (III) adding texture metrics derived from the grey-level covariance co-occurrence matrix (GLCM); and (IV) including topographic variables derived from a digital terrain model. Both methods stand out for their excellent results, reaching an average overall accuracy of 88% for support vector machine and 90% for random forest. However, it is statistically shown that random forest performs better on this type of landscape. Furthermore, incorporating Digital Terrain Model (DTM)-derived metrics and texture measures was critical for the substantial improvement of SVM and RF. Although DTM did not increase the accuracy in SVM, this study makes a methodological contribution to the monitoring and mapping of water bodies’ landscapes in coastal cities with weak governance and data scarcity in coastal management.
Three-dimensional urban vegetation data provide crucial ecological information, but generally feature poor coverage and availability. The spaceborne radar interferometer TanDEM-X delivers a digital elevation model that can be processed to produce urban Canopy Height Models (CHMs), including vegetation height and area per 12-metre cell. To date, these CHMs are underutilised in urban research. This study therefore explores the potential of urban TanDEM-X CHM by combining an expert survey on potential directions for CHM in urban research and a case study conducting and integrating a CHM for climatological research in the city of Yazd, Iran. We complement these empirical analyses with a detailed literature section of the current state of application of TanDEM-X-CHM. The expert survey highlights key research directions and interests for urban CHM implementation. The case study provides an example of implementation based on findings from the expert survey, revealing effects of vegetation height and volume on land surface temperature. Our results underscore the unique characteristics of a TanDEM-X-based CHM, asserting their potential for producing three-dimensional vegetation information crucial for effective research and policy efforts in urban ecology.
Interferometric methods are drivers of the recent exponential growth in the use of synthetic aperture radar (SAR) for monitoring both natural and anthropogenic hazards. Since the first use of interferometric SAR (InSAR) in the late 1990s to detect deformations associated with earthquakes and volcanoes, important developments have improved sensor performance and data-processing capabilities for the extraction of information of interest in geophysical applications. Here, we describe the improvements that have enabled the 3D reconstruction and monitoring of buildings, with reference to their protection against and prevention of risks. We focus on buildings affected by systematic displacements related to natural hazards-specifically, landslides. We address the straightforward use of InSAR methods in the detection and characterization of the hazard as well as in higher-level exploitation for geotechnical and structural studies and, more generally, vulnerability analysis.
Der Wasserstand h ist nach DIN 4049-3 (1994) der lotrechte Abstand eines Punktes des Wasserspiegels über oder unter einem Bezugshorizont, z. B. durch einen Pegelnullpunkt PNP festgelegt (s. Abb. 3.1). Der Wasserstand wird üblicherweise in Meter oder Zentimeter angegeben. Die Kenntnis der Wasserstände eines Gewässers wird u. a. benötigt, umden Durchfluss Q mittels W-Q-Beziehung zu ermitteln (s. Abschn. 5.4),die hydrologischen Verhältnisse eines Einzugsgebietes, insbesondere den Wasserkreislauf und seine wasserwirtschaftlichen Nutzungen bzw. Nutzungsmöglichkeiten, beurteilen zu können,die Nutzung von Gewässern für den Schriftverkehr zu ermöglichen und zu sichern,Melde- und Warndienste, z. B. für Hochwasser, aufzubauen und zu betreiben,den Wasserstand von Gewässern in Hochwasser- und Niedrigwasserzeit zu regulieren,morphologische Veränderungen z. B. des Gewässerbetts, zu erfassen und zu beurteilen,den aktuellen Füllungsstand von Speichersystemen (z. B. Talsperren) als Grundlage für ihre Bewirtschaftung zu erfassen und zu nutzen.
Near-real-time flood maps are essential to organize and coordinate emergency services' response actions during flooding events. Thanks to its capacity to acquire synoptic and detailed data during day and night, and in all weather conditions, Synthetic Aperture Radar (SAR) satellite remote sensing is considered one of the best tools for the acquisition of flood mapping information. However, specific factors contributing to SAR backscatter in urban environments, such as shadow and layover effects, and the presence of water surface–like radar response areas, complicate the detection of flood water pixels. This paper describes an approach for near-real-time flood mapping in urban and rural areas. The innovative aspect of the approach is its reliance on the combined use of very-high-resolution SAR satellite imagery (C-band, HH polarization) and hydraulic data, specifically flood return period data estimated for each point of the floodplain. This approach was tested and evaluated using two case studies of the 2011 Richelieu River flood (Canada) observed by the very-high-resolution RADARSAT-2 sensor. In both case studies, the algorithm proved capable of detecting flooding in urban areas with good accuracy, identifying approximately 87% of flooded pixels correctly. The associated false negative and false positive rates are approximately 14%. In rural areas, 97% of flooded pixels were correctly identified, with false negative rates close to 3% and false positive rates between 3% and 35%. These results highlight the capacity of flood return period data to overcome limitations associated with SAR-based flood detection in urban environments, and the relevance of their use in combination with SAR C-band imagery for precise flood extent mapping in urban and rural environments in a crisis management context.
Land subsidence due to underground resources exploitation is a well-known problem that affects many cities in the world, especially the ones located along the coastal areas where the combined effect of subsidence and sea level rise increases the flooding risk. In this study, 25 years of land subsidence affecting the Municipality of Ravenna (Italy) are monitored using Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques. In particular, the exploitation of the new Sentinel-1A SAR data allowed us to extend the monitoring period till 2016, giving a better understanding of the temporal evolution of the phenomenon in the area. Two statistical approaches are applied to fully exploit the informative potential of the A-DInSAR results in a fast and systematic way. Thanks to the applied analyses, we described the behavior of the subsidence during the monitored period along with the relationship between the occurrence of the displacement and its main driving factors.
Using synthetic aperture radar (SAR) interferometry to monitor long-term millimeter-level deformation of urban infrastructures, such as individual buildings and bridges, is an emerging and important field in remote sensing. In the state-of-the-art methods, deformation parameters are retrieved and monitored on a pixel basis solely in the SAR image domain. However, the inevitable side-looking imaging geometry of SAR results in undesired occlusion and layover in urban area, rendering the current method less competent for a semantic-level monitoring of different urban infrastructures. This paper presents a framework of a semantic-level deformation monitoring by linking the precise deformation estimates of SAR interferometry and the semantic classification labels of optical images via a 3-D geometric fusion and semantic texturing. The proposed approach provides the first "SARptical" point cloud of an urban area, which is the SAR tomography point cloud textured with attributes from optical images. This opens a new perspective of InSAR deformation monitoring. Interesting examples on bridge and railway monitoring are demonstrated.
Urban areas are the primary living environments of human beings, and a frequent focus in Earth observation based analyses. Chances for advancement in this field especially exist in the detection of man-made targets in urban area using synthetic aperture radar (SAR) images. Especially stationarity is a useful parameter in SAR image information extraction that has begun to attract attention in recent years. However, the theory and analytical methods of stationarity in the SAR processing domain are still in the initial stage. In this paper, six types of stationarity in SAR imagery are discussed, and stationarity in azimuth direction is utilized to separate man-made and natural targets in urban areas. Furthermore, a new azimuth stationarity extraction method based on Rician distribution is proposed to adapt to the complex situation in urban areas and to achieve an effective detection result. According to our analyses, the result obtained by the new method proposed shows a 10%-20% higher accuracy than the result based on traditional stationarity extraction methods, and the overall detection accuracy for the method proposed exceeds 80%, which indicates a good performance.
Height estimation of man-made structures in urban and suburban areas is an interesting but challenging problem, especially from single SAR image, which usually occurs in emergency response after crisis. A novel model based height estimation approach is proposed, which finds a best match between parameterized object model and the observed single intensity SAR image data. Firstly, a projection function for single bounce signal return between 3D object model and corresponding characteristic regions in SAR image is constructed, which make the model generation phase very simple and fast. Then, a new likelihood measure for the matching between the height hypothesis and the SAR data is proposed in the respect of image segmentation. The matching procedure is iterated until a global maximum similarity is achieved and then the height estimation is obtained, which is realized by the simulated annealing algorithm. The experiments performed on general terms of buildings in the simulated SAR image data set verify its validity.
With the increasing availability of high resolution SAR imagery like RADARSAT-2 and TerraSAR-X, it becomes interesting to investigate the potential of this type of data for urban applications. There is however a great obstacle in using SAR imagery of urban areas: the corner reflector or "cardinal effect" problem. It is greatly problematic when multiple images of the same scene are taken from different azimuth angle. We propose a novel framework to overcome this problem by using contextual information about road orientation and building position to correct higher than normal pixel intensity caused by corner reflectors.
With the improvements of spaceborne and airborne SAR system resolution, the applications of radar remote sensing has been extended to building 3D geometric information retrieval and reconstruction from urban SAR images, which is the foundation of build-up areas reconstruction and urban analysis. This paper mainly focuses on the problem of building height estimation from a single high resolution (HR) SAR image of urban scenes. A model based method combined with image segmentation framework of building height estimation is proposed. This method optimizes a new likelihood measure between the projection image from 3D geometric model of the buildings and the observed image over the heights hypothesis space. With assumption of the parallelepiped shapes, the SAR building area is partitioned into several regions. The new likelihood criterion then measures both the inner homogeneity of partitioned regions as well as their inter heterogeneity to achieve robust height hypothesis test. The optimization is done by simulated annealing in order to avoid local optimum. The experimental results performed on simulated SAR image data set valid the proposed method. The large amount of SAR data provided by current spaceborne (TerraSAR-X, Cosmo-SkyMed…) and airborne sensors (LYNX, RAMSES…) with metric or sub-metric resolution, offers today the opportunity to extend the analysis of urban remote sensing to individual building objects. The extraction of building height information from SAR image is essential in many applications, ranging from urban expansion monitoring to rapid mapping in case of time critical events (tsunami, earthquake) (1), etc. While some well-known techniques such as radar- grammetry (2-4) or interferometry (5-6) have been applied successfully for building height estimation, several problems still open. The identification and exclusion of shadow or layover areas of building in radargrammetry proves problematic, since they are almost ubiquitous. The usual interferometric approach based on unwrapping tends to be difficult in HR SAR data without external information to regularize unwrapping process. Moreover, quite often only one single-pass acquisition will be available for the region of interest (ROI). Sportouche (7-9) delivered a SAR and optical data fusion framework for height information retrieval, which was crucial for the availability of optical data couple, and still demand a prior DEM and operator's intervention for optical and radar image registration. Guida (10) presented a deterministic approach that invert geometric and electromagnetic models of simple building, for which an extensive prior parameters of building and its surrounding were needed. In (11), Brunner utilized an iterative simulation and matching technique, in which the quality of simulated SAR image was critical for estimation accuracy. Nevertheless, a high performance SAR simulator was usually computationally expensive and may not be always available in operational scenarios. The edge extraction and matching based height retrieval was also discussed in (12). However, the edge discontinuity caused by speckle usually limited the precision of layover or shadow extraction and may lead to biased height estimation. Since building objects generally tend to have very well defined geometries in high-resolution built-up scenes, an alternative model based approach, which is able to inference height parameter from both SAR imaging and building geometric models, can be conceived. In this paper, we generalize and extend the work presented in (9, 11, 12), and propose a novel height extraction method of buildings from single detected SAR imagery. The derivation of the scene parameters from the SAR image data is a classical ill- conditioned inverse problem. Therefore, a project model is constructed as constraint. A series of hypotheses is generated and validated by a likelihood measure between the projection image from a D model of building and the observation under the simulated annealing framework, taking into account the
An approach for the automatic extraction of roads from digital aerial imagery is proposed. It makes use of several versions of the same aerial image with different resolutions. Roads are modeled as a network of intersections and links between these intersections, and are found by a grouping process. The context of roads is hierarchically structured into a global and a local level. The automatic segmentation of the aerial image into different global contexts, i.e., rural, forest, and urban area, is used to focus the extraction to the most promising regions. For the actual extraction of the roads, edges are extracted in the original high resolution image (0.2 to 0.5 m) and lines are extracted in an image of reduced resolution. Using both resolution levels and explicit knowledge about roads, hypotheses for road segments are generated. They are grouped iteratively into larger segments. In addition to the grouping algorithms, knowledge about the local context, e.g., shadows cast by a tree onto a road segment, is used to bridge gaps. To construct the road network, finally intersections are extracted. Examples and results of an evaluation based on manually plotted reference data are given, indicating the potential of the approach.
Two landuse maps and a forest map of three different areas in
Europe were completed with ERS SAR interferometry. The test sites
represent various geomorphological regions with different cover types.
In this article, the mapping algorithms are presented, the results are
summarized, and the potential and limitations of ERS SAR interferometry
for landuse mapping are discussed. Overall, the results suggest that
landuse classification accuracies on the order of 75% are possible with,
in the best case, simultaneous forest and nonforest accuracies of around
80-85%. The presence of topography reduces the performance
The following issues relate to quality assessment of image classification: the classification methods as such, the methods to evaluate the classification results, and the requirements of the application. In this paper, a number of evaluation methods are reviewed, and it is concluded that those based on confusion matrices and the KHAT analysis are the most suited if one is interested in comparing classifiers. The novelty of this paper is that much attention is given to the subjectivity present in every evaluation scheme, and that the concept of accuracy is extended to quality by creating the link between accuracy, objectives, and costs. A protocol is proposed for quality assessment related to the economical reality. An example based on a hypothetical data set shows that the economic cost of misclassification can be high, and that it may be advantageous for the user to reconsider either the objectives, the type of data used, or other aspects of the remote-sensing system that he uses to produce the map.
The movements produced by the 1992 earthquake in Landers, California are mapped using SAR interferometry. An interferogram is constructed by combining topographic information with SAR images obtained by the ERS-1 satellite before and after the earthquake. It is shown that the observed changes in the range from the ground surface to the satellite agree well with the slip measured in the field, with the displacements measured by surveying, and with the results of Okada's (1985) elastic dislocation model. This interferogram provides a denser spatial sampling than surveying methods and a better precision than earlier space imaging techniques.
Image fusion is a technique that is used to combine the spatial structure of a high resolution pan- chromatic image with the spectral information of a low resolution multispectral image to produce a high resolution multispectral image. In this paper, first results of fusion experiments with TerraSAR- X and optical multispectral image data will be presented. To generate fused images, use was made of the Ehlers fusion, a fusion technique that is developed for preserving maximum spectral informa- tion. As a result of a changeable filter setting within the Ehlers fusion, three differently fused im- ages were created. To assess the quality of the fusion process, visually and quantitative analyses were performed. First, each band of the fused image was visually compared to the respective original multispectral band for preservation of the original spectral characteristics. Then, the identi- cal band combinations of the fused and original images were compared, such as true color or false color infrared composites. For the spatial resolution evaluation of the fused images, their spectral bands of the fused images were compared to the high resolution TerraSAR-X image. Particular attention was paid to changes of contrast and gray values near edges like streets and buildings. Due to the fact that visual comparison is very subjective; the fused images were also evaluated using quantitative and statistical methods. Techniques that were employed included correlation analysis, calculation of per-pixel deviation, structure similarity index (SSIM), high pass filtering, and edge detection techniques. Best overall performance was achieved by a filter design that repre- sented a compromise between ultimate spatial enhancement and optimum color preservation.
Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. Many physical and geometric parameters of the imaged scene contribute to the grey value of a SAR image pixel. Scene inversion suffers from this high ambiguity and requires SAR data taken at different wavelength, polarization, time, incidence angle, etc.
Interferometric SAR (InSAR) exploits the phase differences of at least two complex-valued SAR images acquired from different orbit positions and/or at different times. The information derived from these interferometric data sets can be used to measure several geophysical quantities, such as topography, deformations (volcanoes, earthquakes, ice fields), glacier flows, ocean currents, vegetation properties, etc.
This paper reviews the technology and the signal theoretical aspects of InSAR. Emphasis is given to mathematical imaging models and the statistical properties of the involved quantities. Coherence is shown to be a useful concept for system description and for interferogram quality assessment. As a key step in InSAR signal processing two-dimensional phase unwrapping is discussed in detail. Several interferometric configurations are described and illustrated by real-world examples. A compilation of past, current and future InSAR systems concludes the paper.
The new class of high resolution spaceborne SAR systems, like TerraSAR-X and COSMO-Skymed opens new possibilities for SAR interferometry. The 1m resolution is particularly helpful when 2D, 2.5D, 3D, or 4D (space-time) imaging of buildings and urban infrastructure is required, where the non-interferometric interpretation of SAR imagery is difficult. Structure and defor-mation of individual buildings can be mapped, rather than only coarse deformation patterns of areas. The paper demonstrates several new developments in high resolution SAR interferometry using Ter-raSAR-X as an example. Of particular interest is the very high resolution spotlight mode, which requires some care in interferometric processing. Results from interferometry, Persistent Scatterer Interferometry (PSI), and tomographic SAR in urban environment are presented. The high resolution of TerraSAR-X also supports accurate speckle and feature tracking. An example of glacier monitoring is shown and discussed.
In this paper, we describe an extension of the automatic road extraction procedure developed for single SAR images towards multiaspect SAR images. Multi-aspect images illuminate the same scene, but from different directions. For the combination of the extracted information, a fusion technique is introduced. Each road segment is assessed according to its direction compared to the direction of the illumination. Due to the side-looking geometry of SAR, the visibility of roads is often limited by adjacent trees or building rows. Roads in viewing direction are less affected by shadow and layover effects from neighbouring objects than roads across the viewing direction. Road segments are evaluated, according to its expected visibility. Roads in viewing direction are therefore higher evaluated, than roads running in azimuth direction. The fusion technique is demonstrated on two sub-urban SAR scenes. The results show the potential of the proposed fusion strategy; with the use of two or more views, the resulting road network is more complete and more correct than for each single image.
This paper deals with automatic road extraction from SAR imagery. In general, automatically extracted road networks are not complete, i.e., gaps remain in the erxtracted network. Especially in SAR imagery many objects occlude road sections and cause gaps, due to the side looking geometry of the SAR sensor. In this paper an approach for automatic road extraction is proposed that is optimized for rural areas by using additional explicitly modeled knowledge about roads and the context of roads. Roads are modeled as a network. Context of roads is hierarchically structured into a global and a local level. Local context objects like trees or vehicles can interfere road extraction due to the layover effect or the motion, but they can also support it. It is shown that the incorporation of local context objects into the extraction improves the results by bridging smaller gaps. Though the approach is restricted to rural areas, other global context regions can provide additional information, too. Here, urban areas are used to deliver confident seed information for the road network generation, because it is the characteristic and function of roads to connect urban areas with each other. With this information a more complete network is extracted. Furthermore, a new approach for highway extraction is proposed based on a multi-scale modeling. Because of the larger dimensions of highways and the more salient substructures, like the crash barriers, a more detailed model and extraction strategy is needed. Finally, examples and results are given, showing the potential of using context information and explicit modeling of roads for automatic road extraction from SAR imagery.
Most attempts at urban analysis using remotely sensed imagery lack the capabilities necessary to define the detailed geometry and differentiate the textures of the complex urban landscape. This paper presents a proof-of-concept study of the potential for integrative analysis of Interferometric Synthetic Aperture Radar (IFSAR) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery for a study area in Los Angeles, California. Recent advances in the use of interferometric radar allow the definition of high resolution, three-dimensional (3D) geometry of surface features, topography, and impervious surfaces in urban areas. The radar analysis is enhanced using hyperspectral imagery to mask surfaces adjacent to structures in order to assist in the determination of baseline topography and segmentation of building footprints for improved geometric measurement of the complex urban area.
Today's airborne (Memphis, AeS-1, Ramses) and space borne (TerraSAR-X, CosmoSkyMed, Radarsat) SAR sensors provide very high resolution imagery independent of daylight and cloud coverage. Space borne systems achieve geometrical resolutions of down to one meter while airborne sensors are capable of acquiring images with sub metric resolution. In this kind of data, urban objects like buildings and bridges become visible in much detail. However, due to the side-looking SAR sensor principle, layover and occlusion hamper the interpretation particularly in urban scenes. One possibility to overcome this drawback is the use of additional information from high resolution optical imagery. In this paper, first findings of a long term project using both optical and SAR imagery for the modelling and extraction of bridges are presented. The focus is on bridges because they play a key role as connecting parts of man-made infrastructure and are of high importance in case of rapid natural hazard response. Differences between bridges over water and bridges over land are explained. Furthermore, concepts for estimating bridge heights from of a single SAR image and by means of combined optical and SAR imagery are derived.
This article studies the improvement of road detection using 2 SAR images of the same area taken with 2 different orientations. In the first part, some remarks about the visual appearance of the network in both images are made. In the second part, two experiments for merging the two images are presented, and the obtained results are compared.
The new generation of satellite and aircraft sensors provides image data of very and ultra high resolution which challenge
conventional aerial photography. The high-resolution information, however, is acquired only in a panchromatic mode whereas
the multispectral images are of lower spatial resolution. The ratios between high resolution panchromatic and low resolution
multispectral images vary between 1:2 and 1:8 (or even higher if different sensors are involved). Consequently, appropriate
techniques have been developed to merge the high resolution panchromatic information into the multispectral datasets. These
techniques are usually referred to as pansharpening or data fusion. The methods can be classified into three levels: pixel
level (iconic) fusion, feature level (symbolic) fusion and decision level fusion. Much research has concentrated on the iconic
fusion because there exists a wealth of theory behind it. With the advent of object or segment oriented image processing techniques,
however, feature based and decision based fusion techniques are becoming more important despite the fact that these approaches
are more application oriented and heuristic. Within this context, the integration of GIS based information can easily be accomplished.
The features can come from a specific segmentation algorithm or from an existing GIS database. Within the context of feature
and decision based fusion, we present two exemplary case studies to prove the potential of decision and feature based fusion.
The examples include
In general, object recognition from images is concerned with separating a connected group of object pixels from background
pixels and identifying or classifying the object. The indication of the image area covered by the object makes information
which is implicitly given by the group of pixels, explicit by naming the object. The implicit information can be contained
in the measurement values of the pixels or in the locations of the pixels relative to each other. While the former represent
radiometric properties, the latter is of geometric nature describing the shape or topology of the object.
This chapter reviews the urban applications of Persistent Scatterer Interferometry (PSI), the most advanced type of differential
interferometric Synthetic Aperture Radar techniques (DInSAR) based on data acquired by spaceborne SAR sensors. The standard
DInSAR techniques exploit the information contained in the radar phase of at least two complex SAR images acquired at different
times over the same area generating interferograms or interferometric pairs. For a general review of SAR interferometry, see
Rosen et al. (2000) and Crosetto et al. (2005). A large part of the DInSAR results obtained in the 1990s has been achieved by using the standard DInSAR configuration,
which in some cases is the only one that can be implemented due to the limited SAR data availability.
The actual process of rapid urbanisation is associated with various e cological, social and economic changes in both, the urban area and the surrounding natural environment. In order to keep up with the effect s and impacts of this development, effective urban and regional planning requires accurate and up-to-date information on the urban dyna mics. Recent studies have demonstrated the applicability of high resolution optical satellite data for the acqui sition of spatial and socio-economic information. In contrast, radar imagery has hardly been employed for these purposes so far. The future TerraSAR-X will provide radar data with a ground resoluti on comparable to existing high resolution optical satellite sensors. Thus, it will afford detailed urban analysis based on spac eborne radar imagery for the first time. The detection concept presented here serves as a preliminary investigation of the potentia l use of TerraSAR-X data in the context of urban applications. It introduces a robust approach towards an automated detection of built-up area s using data acquired by the Experimental Synthetic Aperture Radar (E-SAR) system of the German Aerospace Center (DLR). For that purpose different data sets of single-polarised X- band imagery are analysed in an object-oriented classification. A robust object-oriented analysis strongly depends on accurate and relia ble image segmentation. Thus, a classification-based optimisation procedure to stabilise and improve the initial image segm entation step is introduced. Subsequently the identification of built-up areas is performed on the basis of three image segmentation levels in different spatial scales. Here, contextual and text ural features along with shape-related and hierarchical characteristi cs play a major role. Finally, the transferability and robustne ss of the presented approach is illustrated by applying the developed classific ation scheme to E-SAR data of three complete flight tracks.
We present here a new InSAR persistent scatterer (PS) method for analyzing episodic crustal deformation in non-urban environments, with application to volcanic settings. Our method for identifying PS pixels in a series of interferograms is based primarily on phase characteristics and finds low-amplitude pixels with phase stability that are not identified by the existing amplitude-based algorithm. Our method also uses the spatial correlation of the phases rather than a well-defined phase history so that we can observe temporally-variable processes, e.g., volcanic deformation. The algorithm involves removing the residual topographic component of flattened interferogram phase for each PS, then unwrapping the PS phases both spatially and temporally. Our method finds scatterers with stable phase characteristics independent of amplitudes associated with man-made objects, and is applicable to areas where conventional InSAR fails due to complete decorrelation of the majority of scatterers, yet a few stable scatterers are present.
The development of high (2m) resolution interferometric SAR
(IFSAR) instrumentation makes extraction of man made and natural urban
structures feasible. In particular, the authors consider building
extraction from imagery of an urban/industrial area. IFSAR imagery are
particularly well suited for this task because these data include the
measured elevation as well as the coherence and intensity of the back
scattered radiation. Gradients in the IFSAR elevation correspond
directly to elevation edges. Coherence and intensity data can be
combined to give specific information about the scattering properties of
the viewed surface. The disadvantage of IFSAR imagery is that these data
are typically of lower resolution and contain greater noise than other
data such as optical photography, also the data contain specific
artifacts that must be removed. Indeed, the motivation for building and
tree extraction behind this work is the need to remove noise and
artifacts from the IFSAR data. Techniques for removing artifacts that
are peculiar to IFSAR data are particularly discussed
The new generation of satellite and aircraft sensors provides image data of very and ultra high resolution which challenge conventional aerial photography. The high-resolution information, however, is acquired only in a panchromatic mode whereas the multispectral images are of lower spatial resolution. The ratios between high resolution panchromatic and low resolution multispectral images vary between 1:2 and 1:8 (or even higher if different sensors are involved). Consequently, appropriate techniques have been developed to merge the high resolution panchromatic information into the multispectral datasets. These techniques are usually referred to as pansharpening or data fusion. The methods can be classified into three levels: pixel level (iconic) fusion, feature level (symbolic) fusion and decision level fusion. Much research has concentrated on the iconic fusion because there exists a wealth of theory behind it. With the advent of object or segment oriented image processing techniques, however, feature based and decision based fusion techniques are becoming more important despite the fact that these approaches are more application oriented and heuristic. Within this context, the integration of GIS based information can easily be accomplished. The features can come from a specific segmentation algorithm or from an existing GIS database. Within the context of feature and decision based fusion, we present two exemplary case studies to prove the potential of decision and feature based fusion.The examples include: • Decision based integration of panchromatic high resolution data with multispectral images for the identification of settlement areas; • Rapid image enhancement merging GIS and multispectral satellite data; and • NDVI based segmentation.
This study compared spaceborne radar and radar-derived products with optical data for urban delineation. RADARSAT and Landsat Thematic Mapper (TM) multispectral data were assessed independently and in combination. The primary methodology was supervised spectral signature extraction and the application of a maximum-likelihood statistical decision rule to classify surface features in the Kathmandu Valley, Nepal. Relative accuracy of the resultant classifications was established by comparison to ground-truth information. Both radar post-classification smoothing and Variance texture measures were improvements over the poor results achieved with the unfiltered, original radar data. Speckle reduction procedures were found to be very advantageous. Combinations of radar-derived products greatly improved results, achieving an overall accuracy of nearly 90 percent. The best overall accuracy was achieved with the merger that included a texture image derived from despeckled radar and the despeckled original radar. The radar and radar-derived combination achieved much better results than did the TM and were comparable to a combined radar and TM data classification. The systematic strategy of this study, determination of the best individual method before introducing the next procedure, was effective in managing a very complex, almost infinite set of analysis possibilities.
Combining data from different sensors for visual or classification analysis is a common task in remote sensing. The first step is normally to register the images which may be considered geometric integration; the accuracy of this step is important to create a valuable final hybrid
image. This paper addresses geometric integration and introduces a new method for automatically registering two dissimilar images, such as, a radar image and an optical image with high accuracy. Pre-registration of the two images to within a specified tolerance is required. In our examples,
this tolerance is up to 17 pixels (at the scale of the higher resolution image) and may be achieved by, for example, visually located control points. The described approach then uses large-scale edge gradient contours in a process that automatically locates candidate control points on the
contours. The points are selected using a cost function that measures the degree of match between all possible pairs of points. Numerous control points (typically around 50 pairs) are found from matched pairs of gradient contours and used in a global, rubber sheet, polynomial warp to refine
the registration. This approach is applied to register a Synthetic Aperture Radar (SAR) image (ERS2, 12.5 m pixels) and a Thematic Mapper (TM) optical image (Landsat-5, 28.5 m pixels) automatically. Several examples with different scene content are shown to validate the approach and discussed
in terms of residual registration error and processing efficiency.
The objective of this research was to evaluate the synergistic effects of multitemporal European remote sensing satellite 1 (ERS-1) synthetic aperture radar (SAR) and Landsat thematic mapper (TM) data for crop classification using a per-field artificial neural network (ANN) approach. Eight crop types and conditions were identified: winter wheat, corn (good growth), corn (poor growth), soybeans (good growth), soybeans (poor growth), barley/oats, alfalfa, and pasture. With the per-field approach using a feed-forward ANN, the overall classification accuracy of three-date early- to mid-season SAR data improved almost 20%, and the best classification of a single-data (5 August) SAR image improved the overall accuracy by about 26%, in comparison to a per-pixel maximum-likelihood classifier (MLC). Both single-date and multitemporal SAR data demonstrated their abilities to discriminate certain crops in the early and mid-season; however, these overall classification accuracies (<60%) were not sufficiently high for operational crop inventory and analysis, as the single-parameter, high-incidence-angle ERS-1 SAR system does not provide sufficient differences for eight crop types and conditions. The synergy of TM3, TM4, and TM5 images acquired on 6 August and SAR data acquired on 5 August yielded the best per-field ANN classification of 96.8% (kappa coefficient = 0.96). It represents an 8.3% improvement over TM3, TM4, and TM5 classification alone and a 5% improvement over the per-pixel classification of TM and 5 August SAR data. These results clearly demonstrated that the synergy of TM and SAR data is superior to that of a single sensor and the ANN is more robust than MLC for per-field classification. The second-best classification accuracy of 95.9% was achieved using the combination of TM3, TM4, TM5, and 24 July SAR data. The combination of TM3, TM4, and TM5 images and three-date SAR data, however, only yielded an overall classification accuracy of 93.89% (kappa = 0.93), and the combination of TM3, TM4, TM5, and 15 June SAR data decreased the classification accuracy slightly (88.08%; kappa = 0.86) from that of TM alone. These results indicate that the synergy of satellite SAR and Landsat TM data can produce much better classification accuracy than that of Landsat TM alone only when careful consideration is given to the temporal compatibility of SAR and visible and infrared data.
A full understanding of radar backscatter from urban areas is necessary in order to develop a robust methodology for monitoring and classifying urban characteristics using remotely sensed Synthetic Aperture Radar images.This paper examines the dominant backscattering mechanisms such as single bounce from roofs, double bounce from wall-ground structures and possibly triple bounce from wall-wall-ground structures, and their relative contributions to the backscatter. With the use of quad-polarized image data such as those acquired by the NASA/JPL AirSAR system, the backscatter can be decomposed into components caused by different backscattering mechanisms, offering a promise for urban monitoring and classification.
Leading-edge airborne synthetic aperture radar (SAR) sensors provide spatial resolutions on the order of a decimetre. In such data, many features of urban objects can be identified, which were beyond the scope of radar remote sensing before. The impact of high-resolution SAR data on the analysis of urban scenes is discussed. An example for the new quality of the appearance of buildings in such data is given and interpreted. The fine level of detail opens the opportunity to reconstruct the structure of man-made objects (e.g. buildings). The problem of the high computational load required for the processing and the image analysis of high-resolution SAR data is discussed. Finally, the topics geocoding and data fusion using different kinds of reference elevation data are addressed.
This paper deals with the automatic extraction of building outlines using a pair of optical and synthetic aperture radar (SAR) images. The aim is to define areas of interest for building height reconstruction in radargrammetric or interferometric applications. Since high resolution optical satellite images are now easily available, such methods merging SAR and optical information could be useful to improve 3D SAR reconstruction (the optical image giving only information on the scene organization). Both SAR and optical data bring complementary information about the building presence and shape. The proposed method is divided into two main steps: first, extraction of partial potential building footprints on the SAR image, and then shape detection on the optical one using the previously extracted primitives (lines). Two methods of shape detection have been developed, the simplest one finding the “best” rectangular shape and the second one searching for a more complicated shape in case of failure of the first one. Results for an industrial area acquired with two incidence angles for the SAR image are presented and analyzed.
Geo-information technology offers an opportunity to support disaster management: industrial accidents, road collisions, complex emergencies, earthquakes, fires, floods and similar catastrophes (for example the recent huge disaster with the Tsunami in South-East Asia on 26 December 2004). Access to needed information, facilitation of the interoperability of emergency services, and provision of high-quality care to the public are a number of the key requirements.
Such requirements pose significant challenges for data management, discovery, translation, integration, visualization and communication based on the semantics of the heterogeneous (geo-) information sources with differences in many aspects: scale/resolution, dimension (2D or 3D), classification and attribute schemes, temporal aspects (up-to-date-ness, history, predictions of the future), spatial reference system used, etc.
The book provides a broad overview of the (geo-information) technology, software, systems needed, used and to be developed for disaster management. The book provokes a wide discussion on systems and requirements for use of geo-information under time and stress constraints and unfamiliar situations, environments and circumstances.
This letter presents an improvement of an already proposed neural classifier, designed to exploit multiband data over urban environments. The original classifier, based on an Adaptive Resonance Theory (ART) network followed by a fuzzy clustering step, is here improved by directly using a neuro-fuzzy approach, the fuzzy ARTMAP neural network. We show that significant advantages in the classifications could be obtained by tuning the fuzzy ARTMAP learning parameters. Overall accuracy has increased on the same dataset of aerial and Synthetic Aperture Radar (SAR) images of the original work. Moreover, the proposed change in the original classifier structure reduces the implementation complexity and increases its capability to adapt to new inputs. To demonstrate the robustness of this new approach, we offer results on a multiband AIRSAR dataset (C-, P- and L-band images) over the urban area of Broni, northern Italy.
In this study the relationship between urban land use features and radar polarization was examined. Statistical tests were applied to density readings of HH-and HV-polarized X-band SAR imagery to determine: (1) differences in signal return among urban land use categories within a single polarization and (2) variations in signal return between polarizations for individual land use categories. Only one category produced a statistical difference between polarizations. Although some categories were separable on both polarizations, others were separable only on a single polarization. Possible reasons are discussed along with an observed clustering of classes by signal response/grey tone.
This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for extracting landuse/land-cover information in the rural-urban fringe of the Greater Toronto Area (GTA) using various image processing techniques and classification algorithms. Five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. The major landuse/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. The best result was achieved for combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) (overall accuracy: 89.7% and Kappa: 0.886). These high accuracies indicated that RADARSAT fine-beam SAR has the potential for operational landuse/land-cover mapping in urban environments.
There are nowadays many kinds of remote sensing sensors: optical sensors (by this we essentially mean the panchromatic sensors),
multi-spectral sensors, hyper-spectral sensors, SAR (Synthetic Aperture Radar) sensors, LIDAR, etc. They have all their own
specifications and are adapted to different applications, like land-use, urban planning, ground movement monitoring, Digital
Elevation Model computation, etc. But why using jointly SAR and optical sensors? There are two main reasons: first, they hopefully
provide complementary information; secondly, SAR data only may be available in some crisis situations, but previously acquired
optical data may help their interpretation.
Advanced radar sensors are able to deliver highly resolved images of the earth surface with considerable information content,
as polarimetric information, 3-D-features and robustness against changing environmental and operational conditions. This is
possible also under adverse weather conditions, where electro-optical sensors are limited in their performance.
With the development and launch of new sophisticated Synthetic Aperture Radar (SAR) systems such as Terra SAR-X, Radarsat-2
and COSMO/Skymed, urban remote sensing based on SAR data has reached a new dimension. The new systems deliver data with much
higher resolution than previous SAR satellite systems. Interferometric, polarimetric and different imaging modes have paved
the way to new urban remote sensing applications. A combination of image data acquired from different imaging modes or even
from different sensors is assumed to improve the detection and identification of man-made objects in urban areas. If the extraction
fails to detect an object in one SAR view, it might succeed in another view illuminated from a more favorable direction.
The simulation of synthetic aperture radar (SAR) data is a widely used technique in radar remote sensing. Using simulations,
data from sensors which are still under development can be synthesized. This provides data for developing image interpretation
algorithms before the real sensor is launched. Simulations can further create simulated images from precisely defined scenes.
They can deliver simulated images of any object of interest from various orbits, at a wide range of angles, using different
wavelengths.
The extraction of 3D city models is a major issue for many applications, such as protection of the environment or urban planning
for example. Thanks to the metric resolution of new SAR images, interferometry can now address this issue. The evaluation
of the potential of interferometry over urban areas is a subject of main interest concerning the new high-resolution SAR satellites
like TerraSAR-X, SAR Lupe, CosmoSkymed. For instance, TerraSAR-X spotlight interferograms provides very accurate height estimation
over buildings (Eineder et al. 2009).
With TerraSAR-X, Germany will launch its first dual-channel SAR satellite mission
in the year 2006. Besides other commercial applications TerraSAR-X should also
be used as a demonstrator for traffic monitoring from space. This paper revises the
theoretical background of traffic monitoring with space-based SARs and applies the
conclusions to a performance analysis of the TerraSAR-X traffic monitoring system.
As it is well-known, an object moving with a velocity deviating from the assump-
tions incorporated in the focusing process of the synthetic aperture radar (SAR)
principle will generally appear both displaced and blurred in the image. To study
the impact of these (and related) distortions in focused SAR images, the analytic
relations between an arbitrarily moving point scatterer and its conjugate in the SAR
image have been reviewed and adapted to the dual-channel satellite specifications
of TerraSAR-X. To be able to monitor traffic under these boundary conditions in
real-life situations, a specific detection scheme is proposed. This scheme integrates
complementary detection and velocity estimation algorithms with knowledge de-
rived from external sources as, e.g., road databases. The main focus of this article
lies on an extensive analytical and empirical accuracy analysis for both vehicle detec-
tion and velocity estimation. The accuracy analysis includes a theoretical accuracy
evaluation and a validation with real data.
The permanent scatterer technique invented at POLIMI has meanwhile developed into a remarkable operational method. It facilitates innovative data products such as urban subsidence maps or atmospheric delay measure-ments and permits new geophysical applications. The accuracy and validity of this techniques has been demonstrated in several projects at DLR. Due to the outstanding availability of data, time series of this technique have mainly been produced from data of the compatible satellite sensors ERS-1 and ERS-2. These time series can even span a continuous time range of about twelve years. This long-term observation enables the monitoring of displacements with millimetre accuracy and even facilitates the detection of seasonal periodic effects. The sensor ERS-1 made its last acquisition in 2000. The similarly constructed successor ERS-2 still moni-tors the Earth's surface even after nine years of operation. But recent acquisitions are unfortunately not suited for general interferometric applications. The reason is a heavily varying Doppler centroid frequency due to failures of gyros. The ERS-2 successor ENVISAT/ASAR is able to pursue the unique continuity in the monitoring of urban areas. But it operates with a slightly different radar frequency compared to the ERS sensors. Consequently the interferometric principle becomes more complicated and the processing has to be modified. We will present the required changes for the permanent scatterer cross interferometry on the developed scientific permanent scatterer system.
We present a framework for scene understanding from interferometric synthetic aperture radar data that is based on Bayesian machine learning and information extraction and fusion. A generic description of the data in terms of multiple models is automatically generated from the original signals. The obtained feature space is then mapped to user semantics representing urban scene elements in a supervised step. The procedure is applicable at multiple scales. We give examples of urban area classification and building recognition of Shuttle Radar Topography Mission data and of building reconstruction from submetric resolution Intermap data.
The resolution of ERS-1 images should let allow man-made
structures like urban areas to be detected. After a brief survey of the
captor response to urban objects, the authors propose a method to detect
urban areas. They illustrate the results obtained on two typical
landscapes: European agricultural hilly landscapes and tropical zones.
Some urban detections are presented both on Aix-en-Provence and Kourou
towns in French Guyana
The spatial resolution of state-of-the-art synthetic aperture radar sensors enables the structure analysis of urban areas. The appearance of buildings in magnitude images in settlements is dominated by the effects of the inherent oblique scene illumination. In urban residential districts, salient pairs of parallel lines of bright magnitude are often caused by direct reflection and double-bounce signal at gable-roofed buildings. In this letter, the magnitude and interferometric phase signature of gable-roofed buildings are discussed to extract reliable building features for reconstruction. The analysis contains signature changes by varying illumination and building geometry. The presented approach is aiming at the reconstruction of gable-roofed buildings by a knowledge-based analysis considering the discussed effects. The reconstruction results are assessed by using a high-resolution LIDAR surface model as ground truth.
High-resolution synthetic aperture radar (SAR) played an important role in the disaster management efforts after the Wenchuan Earthquake on May 12, 2008. SAR data was used for damage assessment, risk monitoring, and for the continuous monitoring of the so-called quake lakes. Landslides, triggered by the earthquake and the aftershocks, devastated large areas, killing thousands of people. Landslides formed natural dams, blocking rivers and leading to 34 so-called quake lakes, endangering millions of inhabitants and rescue workers downstream. SAR, amongst other sensor systems, was used to detect landslides and monitor those quake lakes. SAR can also assist the risk analysis by early surveying landslide prone areas and supporting the risk management and disaster reduction approaches.