18 October 2007 PCA vs. ICA decomposition of high resolution SAR images: application to urban structures recognition
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Abstract
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.
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Houda Chaabouni, Houda Chaabouni, Mihai Datcu, Mihai Datcu, } "PCA vs. ICA decomposition of high resolution SAR images: application to urban structures recognition", Proc. SPIE 6746, SAR Image Analysis, Modeling, and Techniques IX, 674608 (18 October 2007); doi: 10.1117/12.739027; https://doi.org/10.1117/12.739027
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