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—We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multi-plicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our(More)
—This paper presents an application of the recent advances in the field of spherically invariant random vector (SIRV) modeling for coherency matrix estimation in heterogeneous clutter. The complete description of the polarimetric synthetic aperture radar (POLSAR) data set is achieved by estimating the span and the normalized coherency independently. The(More)
—The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topag-raphy Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital(More)
— Discontinuous objects, such as buildings, produce shadows in SAR images. Shadows are striking features which greatly help in the image understanding. Because of the high density of buildings in urban areas, shadows cover a large part of the image and provide a major hint to build a map of the city. A straightforward use of the shadows is to determine the(More)