Semi-automatic fast recognition of areas of interest for SAR image interpretation

The framework of this study is focused on semiautomatic fast recognition of areas of interest for SAR images. The intended goal is to label regions in an image as fastly as possible, into classes significant for a given application. The proposed approach defines the information extraction as a two- level procedure. First, a segmentation technique is applied to obtain an image partition in homogeneous regions, this property being of a crucial interest for the rest of the process. Then, several measures are computed over regions, considered at this level as "objects", in order to identify at which typical class of land-cover they can be attached. Among a large set of measures experimented, we will point out the most pertinent ones for the considered SAR images. In this paper, we focus more on "forest"/"field" areas extraction in SAR images, and show that the discrimination results obtained here are more precise than in a "classical" purely raster classification process. The proposed approach can be extended to discriminate other classes from high resolution SAR images.