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Many polarimetric classification algorithms have been proposed in the literature. It has been shown that the use of spatial information provides better sensitivity for class separation such as forests. In this paper, we wish to address this issue, testing and comparing two polarimetric SAR (Synthetic Aperture Radar) segmentation approaches incorporating contextual information. The first approach is contextual fuzzy clustering based on the use of bias correction defined by a texture feature, while the second one is a Markovian segmentation based on non parametric textural modeling. These approaches have been tested on Oberpfaffenhofen area in Munich and the PolSAR images are acquired in the P band. In both cases, texture considering allowed to improve greatly the rates of good identification.

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ABSTRACT:Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. High spatial resolution images are complex and differ widely in their content, even in the same category. All images are more or less textured and structured. During the last decade, different approaches for the retrieval of this type of images have been proposed. They differ mainly in the type of features extracted. As these features are supposed to efficiently represent the query image, they should be adapted to all kind of images contained in the database. However, if the image to recognize is somewhat or very structured, a shape feature will be somewhat or very effective. While if the image is composed of a single texture, a parameter reflecting the texture of the image will reveal more efficient. This yields to use adaptive schemes. For this purpose, we propose to investigate this idea to adapt the retrieval scheme to image nature. This is achieved by making some preliminary analysis so that indexing stage becomes supervised. First results obtained show that by this way, simple methods can give equal performances to those obtained using complex methods such as the ones based on the creation of bag of visual word using SIFT (Scale Invariant Feature Transform) descriptors and those based on multi scale features extraction using wavelets and steerable pyramids.