Houda Chaabouni-Chouayakh

Coarse-to-Fine Approach for Urban Area Interpretation Using TerraSAR-X Data

With the launch of the German TerraSAR-X system in June 2007, a new generation of high-resolution spaceborne synthetic aperture radar (SAR) data is available, which should facilitate the interpretation of urban environments. Our overall objective in this letter is to provide a semiautomatic tool for urban area interpretation using SAR data. We propose in this letter to fuse different automatic object extractors in order to provide more reliable pieces of interpretation. Our fusion is a coarse-to-fine approach. First, a segmentation of the image is performed to partition the scene into regions having similar properties. The second step consists in detecting bright and dark linear structures which are, in general, linked to the presence of buildings and roads (main classes in urban areas), respectively. The last step gives the final image interpretation using contextual knowledge. Evaluation of the proposed approach in mapping urban areas was carried out using real TerraSAR-X data over the city of Las Vegas in the U.S.

Towards Automatic 3D Change Detection inside Urban Areas by Combining Height and Shape Information

Monitoring of urban areas using remote sensing data requires reliable change detection techniques. While most of the changes are optically visible and easily detectable by an expert user, automatic processes that remain valid even when different kinds of input data are considered, are quite difficult to develop. This paper provides new solutions for semi-automatic 3D change detection of buildings based on the joint use of height and spatial information. It is an attempt to build a reliable scheme for change detection able to process high as well as lower quality Digital Surface Models (DSMs). The subtraction of DSM, computed from stereo pairs acquired at different epochs, provide valuable information about 3D urban change. However, when at least one of the DSMs presents some artifacts, a simple DSM subtraction may result also in the detection of virtual changes. Several post-processing steps are proposed in this paper and adapted to different DSM qualities in order to quantify real changes. Shape features are introduced to describe the geometry of the detected changes and a Support Vector Machine (SVM) classifier is used to differentiate real from virtual changes. Evalua-tion of the proposed approach on object and pixel level in terms of completeness, correctness, overall accuracy, etc is performed, proving its efficiency and relatively high accuracy for different kind of stereo images and consequently different DSM qualities.

Towards automatic 3-D change detection through multi-spectral and digital elevation model information fusion

Various 2-D and 3-D change detection techniques have been developed in the literature in order to monitor changes inside urban areas. Nevertheless, most of these techniques require the interaction of the user either to input data, set parameters or to train classifiers. Automatic unsupervised processes have been seldom tackled since they are very difficult to develop if high accuracies are necessary. This article provides a fully automatic change detection procedure for urban areas monitoring. It exploits at best the information provided by multi-spectral images and Digital Elevation Model (DEM) from two different epochs. A fusion, both at feature and decision levels, is thus proposed in order to automatically detect for each epoch the following land cover classes: buildings, shadowed areas, water bodies, ground, low and high vegetation. Applying such fusion on Ikonos stereo data acquired over an Asian urban area in spring 2006 and winter 2010 and their ensuing DEMs has proved both the efficiency and worth of the joint use of the multi-spectral and height information. A class-for-class comparison is carried out between the two obtained classification maps in order to detect the changes that have occurred between 2006 and 2010 over the studied area. A set of standard evaluation measures widely employed in the literature are finally computed to assess the quality of the proposed procedure.

Backscattering and Statistical Information Fusion for Urban Area Mapping Using TerraSAR-X Data

With the launch of the German TerraSAR-X system, a new generation of high-resolution spaceborne SAR data is available. This opens new perspectives and challenges for the automatic interpretation of urban environments. In fact, a rich information content, previously hidden or not clearly distinguishable in low resolution images such as urban structures (small buildings, vehicles, etc), is now disclosed. However, only proper approaches are able to retrieve automatically this new detailed information. This paper provides solutions for the semi-automatic interpretation and mapping of urban areas using the high resolution provided by TerraSAR-X data. Our solutions take into the increase, with the high resolution, of the visibility of some man-made structures whose scattering response has improved with the high frequency X-band SAR sensor carried by the TerraSAR-X system. They are mainly based on two steps. Firstly, we extract and describe two kinds of information: backscattering and statistical. Secondly, we propose to use information fusion techniques where intelligence has been introduced and enhanced in the way the different information is processed or treated, so that accurate mapping of urban areas could be reached. This mapping is performed through semantic categorization and retrieval of the different scene contents. Promising improvements and real progress toward automatic urban area mapping have been achieved using TerraSAR-X data.

Automatic urban area monitoring using digital surface models and shape features

Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides an automatic approach for 3D change detection based on the joint use of the height and spatial information. In fact, when dealing with urban areas, one possibility to cope with the automatic growth monitoring is the exploitation of the height information relative to the different man-made objects that exist in the scene. The subtraction of Digital Surface Models (DSMs), acquired at different epochs, should thus provide a valuable information about the 3D urban changes occurred in the studied area. However, when at least one of the DSMs presents some artifacts, a simple DSM subtraction could result also in the detection of virtual changes. To remove these virtual changes, we propose in this work to include, in addition to the height information, some shape features that could be of a great help in describing the geometry of the constructed or demolished man-made structures. After that, the Support Vector Machine (SVM) classifier is used to differentiate real from virtual changes. Evaluation of the proposed approach in terms of completeness, correctness, overall accuracy, etc has been performed proving its efficiency and relatively high accuracy.

Linear versus non-linear analysis of relevant scatterers in high resolution SAR images

With the increase of synthetic aperture radar (SAR) sensor resolution, SAR images could include a large variety of interesting real man-made structures. Therefore, a more detailed analysis and a finer description of SAR images of urban areas are needed for a better understanding of the scene. Nevertheless, recognizing scenes using high resolution SAR images requires the capability to identify relevant signal signatures (called also descriptors), depending on variable image acquisition geometry, arbitrary objects poses and configurations. Among feature extraction methods, we propose to use principal components analysis (PCA) and/or independent components analysis (ICA), in order to exploit deeper the nature of SAR signatures. In this paper, both a description of our work and a presentation of our preliminary classification performance results will be provided.

PCA vs. ICA decomposition of high resolution SAR images: application to urban structures recognition

With the increase of the Synthetic Aperture Radar (SAR) sensor resolution, a more detailed analysis and a finer description of SAR images are needed. Nevertheless, when dealing with urban areas, the high diversity of manmade structures combined with the complexity of the scattering processes makes the analysis and information extraction, from high resolution SAR images over such areas, not easily reachable. In general, an automatic full understanding of the scene requires the capability to identify both relevant and reliable signatures (called also features), depending on variable image acquisition geometry, arbitrary objects poses and configurations. Then, since SAR images are formed, by coherently adding the scattered radiations from the components of the illuminated scene objects, we can make the assumption that, the SAR image is a superposition of different sources. Following this approach, one alternative for a better understanding of the HR SAR scenes, could be a combination between the Principal Components Analysis (PCA) and the Independent Components Analysis (ICA) decompositions. Indeed, while the PCA exploits at most the information stored in the sample covariance matrix, the ICA is a de-mixing process whose goal is to express a set of random variables as linear combinations of statistically independent component variables. Such an approach could be useful for the recognition of urban structures, in HR SAR images. In this paper, we compare the Principal Components (PCs) to the Independent Components (ICs). Furthermore, we present some preliminary results on learning and decomposing SAR images, using PCA and ICA.

Automated information extraction from high resolution SAR images: TerraSAR-X interpretation applications

High resolution remote sensing SAR images — such as the image data acquired by the German TerraSAR-X mission — contain a variety of details that have to be extracted by automated processing in order to fully exploit and understand the image content. In particular, the interpretation of man-made structures that are typical of built-up or agricultural areas poses a number of challenges including parameterized image focusing during routine processing, careful despeckling, descriptor and feature extraction, and final classification including specific scattering and 3D effects. Therefore, we propose a set of general sequential as well as dedicated application-dependent processing steps that allow user-oriented classification of high resolution SAR images. We will also report on actual classification results and experiences.

Geometrical and Topological Urban Areas Characterization using TerraSAR-X Data

With the launch of the German TerraSAR-X system in June 2007, a new generation of high-resolution spaceborne synthetic aperture radar (SAR) data is available; which should facilitate the interpretation of urban environments. This article proposes a new automatic tool for geometric and topological urban areas characterization. Our approach is divided into three main steps. First, a bright linear structures detector is applied to extract the geometrical information. Then, a graph-based spatial characterization is used to model the topological relationships between the different detected bright pixels (nodes). Next, a classification by examining the profile of the distributions of the angles between neighboring nodes, is performed in order to label the linked structures. Evaluations of the proposed approach in characterizing the geometry and topology of urban areas were carried out using TerraSAR-X data over three different cities: Las Vegas in the United States, Paris in France and Cairo in Egypt.