[Show abstract][Hide abstract] ABSTRACT: Earth Observation satellites acquire huge volumes of high
resolution images continuously increasing the size of the archives and the variety of EO products. However, only a small part of this data is exploited. In this paper, we present how we take advantage of the TerraSAR-X images of the German Aerospace Center in order to build
applications on top of EO data.
11th Extended Semantic Web Conference (ESWC) 2014; 10/2014
[Show abstract][Hide abstract] ABSTRACT: This paper presents an information extraction and image enhancement technique using single-look complex (SLC) synthetic aperture radar data. The novelty of this method is the proposed complex-domain despeckling stage. Tikhonov-like optimization is used for minimizing the cost function, which consists of a Gauss-Markov random field (GMRF) prior. The GMRF model is used for texture modeling. The texture parameters of the GMRF are estimated using the evidence maximization framework. The experimental results showed that despeckled SLC images have well-preserved textural features, structures, and point scatterers. The phase of the reconstructed image is well preserved and provides good-quality interferograms of high-resolution spotlight images.
IEEE Transactions on Geoscience and Remote Sensing 07/2014; 52(8). · 2.93 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The amount of data available on the internet provides massive additional information for the Earth Observation (EO) imagery. Periodical news, various reports and measurements, pictures or online encyclopedias are just few examples of the existent information. Occasionally, this data offers new perspectives for EO image understanding and interpretation. However, current image analysis do not benefit from the advantage given by external sources. To overcome these drawbacks, the present paper proposes an approach that goes beyond traditional information mining by using a joint image and text analysis. Fast Compression Distance (FCD) is computed to measure the similarities inside a collection of very high resolution images and text files. The main purpose is to discover common patterns within the data, without any a priori assumption, parameter-free, relying on data compression-based techniques. A hierarchical clustering is performed in order to learn about the dependencies between different types of data.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present a quantitative analysis for a rapid mapping scenario that performs a damage assessment of the 2013 floods in Germany. The scenario is created using pre-disaster and post-disaster TerraSAR-X images and an automated annotation system. Our data set is tiled into patches and Gabor filters are used as a primitive feature method applied to each patch separately. An active learning system based on support vector machine is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our evaluation 7 categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. We show that the system supports rapid mapping scenarios (e.g., floods, tsunami, earthquake, etc.) and interactive mapping generation. In addition, with the help of this system, quantitative assessment of disasters can be carried out.
[Show abstract][Hide abstract] ABSTRACT: The latest generation of synthetic aperture radar (SAR) instruments operating in X-band, that is, COSMO-SkyMed (CSK) and TerraSAR-X (TSX), are capable of providing images from coarse resolution to very high resolution. A lot of research effort has been invested in the study and understanding of images obtained from these satellites. However, there is still a huge scope of statistical understanding and comparison of data from both satellites. In this study, we demonstrate some striking similarities between medium resolution data obtained from CSK and TSX Stripmap mode images. Landcover unsupervised clustering using k-means is discussed to further justify our findings. Clustering is carried out using a feature descriptor based on log-cumulants of Gabor coefficients, which was recently proposed by us in earlier studies.
[Show abstract][Hide abstract] ABSTRACT: The goal of the tutorial is the presentation of leading edge concepts, methods and algorithms for information content exploration and extraction from Big Data provided by EO sensors and other related sources.
[Show abstract][Hide abstract] ABSTRACT: The increasing amount of high resolution Earth Observation (EO) data during recent years, has brought the content analysis of the provided data into the spotlight. Most of the current content analysis is based on unsupervised methods (e.g., clustering). However, the structure discovered by these methods is not necessarily human understandable. Moreover, they require some prior knowledge of the structure of the data for initialization. In this paper, we propose an interactive method to discover the semantic structure behind SAR image collections. Thus, we use a modified version of k-means, namely weight-balanced k-means, to perform clustering on the given images. The interaction mechanism allows users to provide the clustering method with relevant knowledge about the structure of the data. Experimental results demonstrate that the structure discovered by the proposed interactive method is closer to human understanding of the data.
EUSAR 2014; 10th European Conference on Synthetic Aperture Radar, Berlin, Germany; 06/2014
[Show abstract][Hide abstract] ABSTRACT: In this paper, complex-valued Markov random field (CMRF) parameters, namely the interaction strength and variance, which have been previously used for noise reduction in interferograms, are proposed for feature extraction from interferometric SAR (InSAR) images. A comparative performance evaluation has been carried out for feature extraction from InSAR and single-look complex (SLC) SAR images. A patch-based classification is performed for a small database of 3 forest classes. Also, a single image is tiled into small patches and unsupervised clustering is performed. The results are compared to that of another MRF-based complex-valued feature vector which consists of complex-mean and covariances.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts. The FCD computes a similarity between two documents through an effective binary search on the intersection set between the two related dictionaries. In the reported experiments the proposed method is applied to documents which are heterogeneous in style, written in five different languages and coming from different historical periods. Results are comparable to the state of the art and outperform traditional compression-based methods.
[Show abstract][Hide abstract] ABSTRACT: In this paper we focus on the extraction and analysis of long-term satellite image time series, and on applications in urban development monitoring. A Satellite Image Time Series (SITS) comprised of more than 100 data sets, covering a time span of over 25 years is extracted from the current Landsat data archives, Then we create stacks of intra-annual images, which are analyzed by applying a series of direct multidate classifications in order to extract the annual built-up surface. The evolution of the built-up areas is then compared against population dynamics for the studied area.
2014 10th International Conference on Communications (COMM); 05/2014
[Show abstract][Hide abstract] ABSTRACT: During the past years, SAR techniques like Persistent Scatterer Interferometry (PSInSAR) have provided hyper-precision sensing at very large spatial scales. The continuous improvement in the quality of PS measurements comes from the constant development of new acquisition geometries embedded in various platforms. In this study we make a comparative assessment of the quality, number and density of Persistent Scatterers obtained using data acquired in different geometric configurations implemented on three platforms - ERS, ENVISAT, TerraSAR-X. All results were obtained by considering individual datasets of the same urban area (Bucharest), with a combined period of acquisitions of 22 years. The analysis is performed in terms of incidence angles, baseline, orbit type, look direction and PS dynamic range.
[Show abstract][Hide abstract] ABSTRACT: In this letter, we carry out a comparative study of statistical models for multilook synthetic aperture radar amplitude images. Ten state-of-the-art statistical models are selected for comparison. To achieve a fair evaluation, we estimate all model parameters using the method of log-cumulants and apply the method to an image pyramid with varying pixel spacing (and resolution). The pyramid is created by different image product generation options. In addition to pixel spacing and resolution, we also consider the homogeneity of a scene for performance evaluation and we apply three performance measures. Through this study, it was found out that some models perform well for all resolutions, while the performance of other models depends heavily on the image content.