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Evaluation of the capabilities of satellite images alsat 2-a for emergency mapping in urban areas, case of the city of m’sila (Algeria)
Abstract
In this paper, we will show the capabilities and limitations of Alsat-2 images in mapping urban areas in emergency situation. The aim of the research was to provide urban information that is geo-referenced in real time during natural disasters (floods, earthquakes). It’s important for fast decision-making so that they will be a necessary support for the estimation of the damages.The following study tests the spatial and radiometric quality of Alsat 2-A images and proposes technical solutions for theiruse in urban mapping. In order to identify and extract the ground realities, we shall describe and make an effort to discern the perceptible aspects of features in urban area. The adopted methodology carries out a statistical analysis of the information extracted from Alsat-2 images of the studied area (the city of M’Sila, Algeria) using classification and segmentation methods. The statistics will show the percentage of the area in relation to the total size of geometric surface and the distance for linear objects. As a result, the quality of the extracted urban texture necessary for urban mapping will be determined. Image processing to improve resolution quality was carried out using merging method. However, the analysis of consistency and discrepancy of these statistics will be done by comparing samples of field data using confusion matrix.
This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbor algorithm. Each of them gave an accuracy respectively of 97.92-95-93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).
With the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.
Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30 m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets.
High spatial resolution (HSR) imagery scene classification has recently attracted increased attention. The bag-of-visual-words (BoVW) model is an effective method for scene classification. However, it can only extract handcrafted features, and it disregards the spatial layout information, whereas deep learning can automatically mine the intrinsic features as well as preserve the spatial location, but it may lose the characteristic information of the HSR images. Although previous methods based on the combination of BoVW and deep learning have achieved comparatively high classification accuracies, they have not explored the combination of handcrafted and deep features, and they just used the BoVW model as a feature coding method to encode the deep features. This means that the intrinsic characteristics of these models were not combined in the previous works. In this paper, to discover more discriminative semantics for HSR imagery, the deep-local-global feature fusion (DLGFF) framework is proposed for HSR imagery scene classification. Differing from the conventional scene classification methods, which utilize only handcrafted features or deep features, DLGFF establishes a framework integrating multi-level semantics from the global texture feature-based method, the BoVW model, and a pre-trained convolutional neural network (CNN). In DLGFF, two different approaches are proposed, i.e., the local and global features fused with the pooling-stretched convolutional features (LGCF) and the local and global features fused with the fully connected features (LGFF), to exploit the multi-level semantics for complex scenes. The experimental results obtained with three HSR image classification datasets confirm the effectiveness of the proposed DLGFF framework. Compared with the published results of the previous scene classification methods, the classification accuracies of the DLGFF framework on the 21-class UC Merced dataset and 12-class Google dataset of SIRI-WHU can reach 99.76%, which is superior to the current state-of-the-art methods. The classification accuracy of the DLGFF framework on the 45-class NWPU-RESISC45 dataset, 96.37 ± 0.05%, is an increase of about 6% when compared with the current state-of-the-art methods. This indicates that the fusion of the global low-level feature, the local mid-level feature, and the deep high-level feature can provide a representative description for HSR imagery.
Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.
The Pansharpening process aims to merge the high spatial resolution of the panchromatic (Pan) image with the spectral information of the multispectral (MS) images. The fused images should represent an enhanced spatial resolution and should preserve the spectral information simultaneously. In the two last decades, many pansharpening algorithms have been implemented in the literature such as IHS, PCA, HPF, etc. Therefore, in comparison with the various conventional methods, our contribution is the conception of a new fusion scheme by combining two different approaches: the Principal Component Analysis (PCA) and the NonSubsampled Contourlet Transform (NSCT). The hypothesis in this combination represent the use of PCA, in first, like statistical approach to obtain from the MS bands the main information, followed by the NSCT as a robust multiresolution and multidirectional approach, to give an optimal representation of the characteristics in the image compared to the classical methods (wavelets), in order to overcome the drawback caused by PCA with the spectral distortion. The focus of this study is to show a new way to combine differently from usual those two approaches, to find a compromise between enhancing the spatial resolution and preserving the spectral information at the same time. The quality of the resulted images has been evaluated by the visual interpretation and the statistical assessment to prove its efficiency compared to other conventional methods.
The article describes characteristics of several relief indicators (surface height, surface slope, surface aspect, height coefficient of surface roughness, slope coefficient of surface roughness, and aspect coefficient of surface roughness) relative to various types of land use in Slovenia and establishes the connection between surface roughness and land use and between relief indicators and individual types of land use.
Land utilization is mainly the one of the human conscious activity in natural environment, Remote sensing is a most common and most effective method of testing the changes in the economic environment and the type of land development. This paper conducts a preliminary analysis based on the land use in Beijing that land-use change in Beijing and the reasons according to data analysis, and pointed out the advantages and disadvantages and limitations of various methods, according to data analysis, land-use change in Beijingland the reasons For future land development and utilization of analytical basis. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.
a et AICHOUCHE BELHADJ-AISSA b . Laboratoire de traitement d'images Faculté d'électronique et d'informatique Université des Sciences et de la Technologie Houari Boumediène Boîte Postale 32, El Alia, Bab-Ezzouar 16111 Alger, Algérie Téléphone : 213 21 24 79 12, poste 806 ; télécopieur : 213 21 24 71 87 Courriel (a, * Correspondant principal Dans cet article, on présente les résultats obtenus par l'exploitation de l'outil variographique pour la description et la discrimination de textures. L'analyse texturale a pour but principal l'extraction de paramètres texturaux qui décrivent la répartition des variations d'intensité dans l'image. Nous proposons une segmentation texturale basée sur les propriétés du variogramme. En particulier, nous montrons que le variogramme communément utilisé en géostatistique peut être exploité pour identifier différentes structures. L'aspect variographique de la géostatistique permet de mesurer cette dépendance entre deux points en fonction de la distance les séparant suivant une orientation particulière. Ainsi, cette dépendance change suivant que la distance augmente ou diminue : les structures répétitives sont alors décrites par des effets de trous dits emboîtements. Les structures d'emboîtement présentes dans un variogramme définissent alors la distance pour laquelle les primitives de texture pourraient être considérés périodiques. Nous avons donc utilisé cette information pour identifier la taille et les directions principales des structures de différentes textures de Brodatz et, ainsi, valider cette approche. Dans un second temps, nous l'avons intégrée dans trois procédures de segmentation, basée sur la comparaison des caractéristiques des variogrammes obtenus pour différentes directions des constructions urbaines et des sols nus pour constituer quatre classes. Des régions urbaines d'Alger (photographies aériennes) ont été utilisées pour l'évaluation des procédures aussi bien pour la segmentation que pour la caractérisation des images urbaines basée sur l'aspect textural de ces dernières. Les résultats obtenus montrent l'intérêt de l'utilisation des mesures variographiques qui permettent de révéler et d'identifier les différents thèmes d'une image urbaine. Mots-clefs : variogramme; texture ; structure ; emboîtement ; portée ; segmentation. TEXTURAL SEGMENTATION OF URBAN IMAGES WITH VARIOGRAPHIC ANALYSIS This paper reports investigations aimed at developing a variographic tool which visually describes or discriminates perceivable qualities of textures. In particular, it will be shown that the variogram measure commonly used in geostatistic can be used to identify different structures. In texture analysis the first and most important task is to extract texture features which most completely embody information about the spatial distribution of intensity variations in an image. We present, here, an approach to texture segmentation using variography. Semi-variograms are essentially graphs measuring the difference between grade values relative to the distance separating the grade sample locations in a particular orientation. This variance therefore changes as the separation distance increases where repetitive structures are described as hole-effects. Since the nesting structure on the semi-variogram defines the separation distance up to which the primitives can be considered repetitive, it can also be used as a tool for identifying the principal directions and size of texture structures. In all likelihood, this will be the distance that exhibits a hole-effect. Using the textural informations provided by the variogram, we performed segmentation schemes based on the comparison of variogram features in the four main directions of land cover and built-up environment to constitute four classes. Urban regions of Alger (aerial photographs) have been used for evaluating the performance of the segmentation scheme proposed in the characterisation and discrimination of the texture aspect of urban images. The results obtained demonstrate the potential usefulness of the variogram measure. Its success is tied directly to the fidelity with which it captures the underlying structure of urban images.
Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.
L'objectif de cette contribution est de présenter les potentialités de la classification orientée-objet d'une image SPOT 5 à 10 mètres de résolution spatiale pour la création d'une couche d'information décrivant l'occupation des sols en zone côtière. Une typologie spécifique en trois niveaux scalaires est mise en place pour le traitement de l'image. La méthode d'extraction de l'information est fondée sur deux étapes principales. L'image est tout d'abord segmentée en régions (segmentation multi-résolution) afin d'obtenir des objets significatifs. Puis des données spectrales, spatiales et contextuelles sur ces objets sont recueillies afin d'orienter la classification en utilisant des fonctions statistiques d'appartenance. A l'issue du traitement, les résultats sont intégrés sous la forme d'une couche vectorielle dans une base d'information géographique pilotée par un SIG. La qualité de la classification est estimée à l'aide de matrices de confusion. Pour l'image entière, la performance globale de la classification est de 88,5 % au niveau le plus grossier, de 78,7 % au niveau intermédiaire et de 75,1 % au niveau le plus fin, avec des résultats hétérogènes selon les classes. Par rapport à une approche par photo-interprétation, la méthode fournit dans des délais relativement courts des informations cohérentes dont la généalogie et la qualité sont décrites. Sur la zone côtière étudiée (Pays de Brest), la démarche procure un état actualisé de l'occupation des sols selon une typologie à trois niveaux, cohérente par rapport aux inventaires de référence (Corine Land Cover et l'Inventaire Permanent du Littoral).
Depuis le début des années 1980, le secteur de l'Information Géographique est devenu un secteur économique en pleine expansion. Avec l'arrivée des images à très haute résolution (résolution inférieure à 5 m), l'offre en données images s'est multipliée et les utilisateurs sont amenés à rechercher l'information la plus adaptée à leurs besoins. Cet article présente les résultats d'une enquête menée en France et dans quelques pays européens, afin d'évaluer le rôle et la place de l'image dans les pratiques des utilisateurs du milieu urbain, d'identifier leurs besoins et de vérifier l'utilité des images satellites à très haute résolution (THR). Cette enquête est comparée à une enquête similaire réalisée aux États-Unis afin d'identifier des « niches » potentielles d'utilisation des images THR. AMÉNAGEMENT, ÉTATS-UNIS, FRANCE, IMAGES SATELLITES, ORTHOPHOTOS, URBANISME ABSTRACT.— Very high resolution imagery, a geographic information source for urban areas? Current situation and prospects.— The geographic information sector has been a fast-growing economic sector since the early 1980s. With Very High Resolution (VHR) imagery (below 5 m), the supply of image data has been multiplied and end-users have to choose the most appropriate information for their needs. This article presents the results of a survey conducted in France and several other European countries to assess the role and place of imagery in urban applications, to identify the needs of urban users and to ascertain the usefulness of VHR images. This work has been compared to an American survey aimed at identifying potential uses for VHR imagery.
L'objectif de cette communication est de présenter une nouvelle méthode permettant la production d'images à haute résolution spatiale, réunissant les qualités de la haute résolution spatiale et de la haute résolution spectrale des satellites d'observation de la Terre et plus particulièrement du capteur HRV de SPOT pour la cartographie urbaine. Les techniques de cartographie urbaine s'appuient sur des algorithmes de classification, dirigée ou non. La haute résolution spatiale ayant, pour cette application, un intérêt prépondérant, des méthodes permettant d'améliorer la résolution spatiale des images multibandes XS ont été développées. Une analyse de ces méthodes a montré qu'elles ne respectaient pas le contenu spectral original des images. La méthode ARSIS (amélioration de la résolution spectrale par injection de structures) a été développée pour fournir des images multibandes à haute résolution spatiale (dans ce cas 10 m). Elle permet la préservation de l'information originale de ces images et, par conséquent, d'améliorer l'efficacité des classificateurs. Elle s'appuie sur la transformée en ondelettes et l'analyse multirésolution, qui sont brièvement présentées. Une démonstration de cette méthode est présentée. Une évaluation quantitative a montré que la méthode ARSIS fournit de meilleurs résultats que les autres méthodes en termes de préservation de la qualité spectrale des images. Des classifications réalisées sur les données originales et sur les images résultant de la fusion sont comparées.
L'observation satellitaire en zone rurale et périurbaine fournit des images hyperspectrales exploitables en vue de réaliser une cartographie ou une analyse du paysage. Nous avons appliqué une classification par maximum de vraisemblance sur des images de zone agricole. Afin de régulariser la classification, nous considérons la modélisation d'image par champs de Markov, dont l'équivalence avec les champs de Gibbs nous permet d'utiliser plusieurs algorithmes itératifs d'optimisation : l'ICM et le recuit simulé, qui convergent respectivement vers une classification sous-optimale ou optimale pour une certaine énergie. Un modèle d'énergie est proposé : le modèle de Potts, que nous améliorons pour le rendre adaptatif aux classes présentes dans l'image. L'étude de la texture dans l'image initiale permet d'introduire des critères artificiels qui s'ajoutent à la radiométrie de l'image en vue d'améliorer la classification. Ceci permet de bien segmenter les zones périurbaines, la forêt, la campagne, dans le cadre d'un plan d'occupation des sols. Trois images hyperspectrales et une vérité terrain ont été utilisées pour réaliser des tests, afin de mettre en évidence les méthodes et le paramétrage adéquats pour obtenir les résultats les plus satisfaisants.
A new segmentation method based on the morphological
characteristic of connected components in images is proposed.
Theoretical definitions of morphological leveling and morphological
spectrum are used in the formal definition of a morphological
characteristic. In multiscale segmentation, this characteristic is
formalized through the derivative of the morphological profile.
Multiscale segmentation is particularly well suited for complex image
scenes such as aerial or fine resolution satellite images, where very
thin, enveloped and/or nested regions must be retained. The proposed
method performs well in the presence of both low radiometric contrast
and relatively low spatial resolution. Those factors may produce a
textural effect, a border effect, and ambiguity in the object/background
distinction. Segmentation examples for satellite images are given
Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of κ statistics of 0.8–0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to κ statistics, which underlines the robustness of the proposed approach.
One of the key geographical developments over the last two centuries has been that of urbanisation. In recent years this has exploded globally, particularly in developing countries. It is essential for governments, planners and researchers in geography and allied fields to understand this process and the main way of being able to do this is to accurately map these changes. The main method of this mapping is Remote Sensing.This up-to-date analysis of the area looks at a wide range of methodologies currently being used to produce and analyse remotely sensed data of urban areas. The authors examine the various techniques used to extract information from digital, multispectral images of urban areas. Donnay and Barnsley then go on to look at the identification of urban forms, the delineation of agglomerations and the development of urban morphology, considering the analysis of integrated data sets and surface models and going on to look at the estimation of human population levels.
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.
Road network extraction from high resolution
satellite images is one of the most important
aspects. In the present paper, research experimentation
is carried out in order to extract the roads from the
high resolution satellite image using image segmentation
methods. The segmentation technique is implemented
using adaptive global thresholding and
morphological operations. Global thresholding segments
the image to fix the boundaries. To compute
the appropriate threshold values several problems are
also analyzed, for instance, the illumination conditions,
the different type of pavement material, the
presence of objects such as vegetation, vehicles, buildings
etc. Image segmentation is performed using morphological
approach implemented through dilation of
similar boundaries and erosion of dissimilar and irrelevant
boundaries decided on the basis of pixel characteristics.
The roads are clearly identifiable in the final
processed image, which is obtained by superimposing
the segmented image over the original enhanced image.
The experimental results proved that proposed
approach can be used in reliable way for automatic
detection of roads from high resolution satellite image.
The results can be used in automated map preparation,
detection of network in trajectory planning for unmanned
aerial vehicles. It also has wide applications
in navigation, computer vision as a predictor-corrector
algorithm for estimating the road position to simulate
dynamic process of road extraction. Although an expert
can label road pixels from a given satellite image
but this operation is prone to errors. Therefore, an
automated system is required to detect the road network
in a high resolution satellite image in a robust
manner.
This paper aims at contributing to the elaboration of new concepts for an efficient and standardized Synthetic Aperture Radar (SAR) based monitoring of floods. Algorithms that enable an automatic delineation of flooded areas are an essential component of any SAR-based monitoring service but are to date quasi non-existent. Here we propose a hybrid methodology, which combines radiometric thresholding and region growing as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. The method relies on the calibration of a statistical distribution of 'open water' backscatter values inferred from SAR images of floods. A radiometric thresholding provides the seed region for a subsequent region growing process. Change detection is included as an additional step that limits over-detection of inundated areas. Two variants of the proposed flood extraction algorithm (with and without integration of reference images) are tested against four state-of-the-art benchmark methods. The methods are evaluated through two case studies: the July 2007 flood of the Severn river (UK) and the February 1997 flood of the Red river (US). Our trial cases show that considering a reference pre- or post-flood image gives the same performance as optimized manual approaches. This encouraging result indicates that the proposed method may indeed outperform all manual approaches if no training data are available and the parameters associated with these methods are determined in a non-optimal way. The results further demonstrate the algorithm's potential for accurately processing data from different SAR sensors.
Conceptual ambiguity of sprawl and lack of consensus among researchers have made the measurement of urban sprawl very difficult, especially from remote sensing data. There are many scales and parameters that are in use to quantify the sprawl; however, many of them are suffered from several limitations. The objective of this article is to document and discuss these sprawl measurement techniques along with their merits and demerits towards the absolute acceptance or rejection. The paper also highlights the scope and direction of future research for the measurement of urban sprawl.
Starting with a group of very different cities from the same cultural region, latin America, a team of young social scientists, thanks to satellite images, obtain a great deal of original material on urban land use and the spatial transformation of the peri-urban area to integrate it with other data for research for a PHD in social sciencis with the objectif of being operational.
With the rapid development of remote sensing, digital image
processing has become an important tool for the quantitative and
statistical analysis of remotely sensed images. These images most often
contain complex natural scenes. The robust interpretation of such images
requires the use of different sources of information about the scenes
under consideration. This paper presents an integrated approach to
robust analysis of SPOT images with the aid of map information as well
as a priori knowledge about the contextual information of images. Markov
random field theory and the Bayes formula are used to formulate the
image analysis problem as a problem of optimization of an objective
function, which in turn permits the application of various existing
optimization algorithms to solve the problem. To increase the robustness
of the result, several techniques are proposed to effectively use map
information and image contextual information. The first one is concerned
with the estimation of the parameters in the objective function with the
help of these two sources of information. The second one is the
integration of map information in Bayes image modeling using a Markov
random field. The third one is a new optimization algorithm which takes
into account map information and image contextual information by means
of a feedback control scheme. The last technique proposed to increase
the robustness of the result is concerned with the fusion of several
(intermediate) analysis results by again using map knowledge and image
contextual information for the estimation of the reliability of these
results
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