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Processing of Synthetic Aperture Radar Images

By Henri Maitre (ed)

Figure 7.2: Colored composition of images from 3 dates obtained by means of the linear vector MMSE estimator (b) where the statistical parameters are calculated over entire regions produced by radiometric segmentation (a)

Figure 7.3: Gauss-Wishart multi-temporal ML classification over regions produced by radiometric segmentation (top) and Gauss-Wishart multi-temporal ML contextual (5 5 pixels) classification on data obtained using the linear vector MMSE estimator (bottom)

Figure 7.5: Van Zyl classification: (a) L-band; (b) C-band; Orgeval site case, AirSAR MAC-Europe'91 data

Figure 7.6: Cloude and Pottier classification: (a) L-band; (b) C-band; Orgeval site case, AirSAR MAC-Europe'91 data

Figure 7.7: Rignot classification: (a) L-band; (b) C-band; Orgeval site case, AirSAR MAC-Europe'91 data

Figure 13.6f: Processing of an interferogram in order to prepare the phase unwrapping. The interferogram is filtered everywhere the phases are detectable; the non-unwrappable areas are marked: in blue for decorrelated areas, in green for discontinuous phase zones

Figure 8.11: Illustration of network detection on an ERS-1 ESA image focused on the Metz area; the cartography set corresponds to the following legend blue: rivers and lakes; red: road network; orange and brown: the decreasingly dense urban environment; green: forest; pink: relief peaks

Figure 8.12: Illustration of network detection on an ERS-1 ESA image focused on the Strasbourg area; the cartography set corresponds to the following legend blue: rivers and lakes; red: road network; orange and brown: the decreasingly dense urban environment; green: forest; pink: relief peaks

Figure 8.13: Detection of the street network in an urban area...

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Processing of Synthetic Aperture Radar Images

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