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 sig natures (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.
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