Combination of texture and shape analysis for a rapid rivers extraction from high resolution SAR images

Abstract

Water surface extraction using satellite images proves to be of great importance due to its utility in several applications such as land use, floods management and monitoring. Among the wide range of sensors orbiting around the earth, Synthetic Aperture Radar (SAR) proves to be a very effective tool in this context due to its robustness to unfavorable weather conditions and its cloud penetrating capabilities. This paper presents a novel rivers extraction method from SAR images mainly based on the combination of a local texture measurement and global knowledge associated to the shape of the object of interest. A local texture measurement is first computed for every pixel of the image to extract homogeneous surfaces, then a mathematical morphology operator is applied to attenuate noise generated by speckle characterizing SAR images. Finally, the surface occupied by the object of interest is compared to the surface associated to the smallest rectangle that encloses this object in order to separate rivers from lakes in the image. The proposed approach was tested on SAR images acquired by RADARSAT-2 satellite from numerous regions of Canada. Our experimental results demonstrate that the proposed approach is robust and effective.