TomoSAR II: 3-D Semantic Scene Interpretation of Tomographic SAR Data

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This project is a follow-up to our previous project entitled “Information extraction methods for Tomographic Synthetic Aperture Radar (TomoSAR) data”. Our objective is to focus on the development of algorithms to obtain an automatic semantic interpretation of the 3-D information contained in tomographic SAR data.To achieve this goal, we will first define a 3-D supervised classification method for tomograms exploiting rich 3-D descriptors, combined with the result of previously established 2-D image classification. This classifier will be adapted to recognize “continuous” objects such as forest, grassland, street and field. To identify “discrete” objects such as buildings and other man-made structures, we will propose two complementary types of detectors. The first one called “perceptual grouping” will consider the grouping of geometric primitives thanks to features inspired from the Gestalt psycho-visual theory. The second one is aimed at localizing and estimating the 3D pose of objects thanks to high-level models describing groups of keypoint features. Our new detection approaches will rely on the generation of an object database obtained thanks to the development of an adapted TomoSAR simulation method, addressing the lack of training data for SAR. All the information from these classifier and detectors will be combined into a single scene interpretation framework thanks to a cost-based formulation. This representation will be exploited in a feedback loop to refine the tomograms and the quality of individual object extraction. The loop will be closed by updating the semantic interpretation from the outputs of the previous steps.Additionally, we will continue to work on spatially adaptive covariance estimation to solve the problems of single-look data filtering and address the issue of model order estimation for filtered data. These steps will be integrated into a TomoSAR processing chain leading to our final scene interpretation.