River Extraction From High-Resolution SAR Images Combining a Structural Feature Set and Mathematical Morphology

Abstract

Water bodies extraction using satellite images is of great importance due to its utility in several applications such as land use planning, floods management, and monitoring. Among the wide range of sensors orbiting the Earth, Synthetic Aperture Radar (SAR) is a very effective tool in this context due to its robustness in the face of unfavorable weather conditions and its cloud penetration capabilities. This paper presents a novel river extraction algorithm from high-resolution SAR images mainly based on the combination of a local texture measurement and the global knowledge associated with the shape of the object of interest. A local texture measurement is first computed at every pixel of the image to extract the homogeneous surfaces contained in the image, and then a mathematical morphology operator is applied to eliminate the noise generated by speckle characterizing SAR images. Finally, the surface occupied by the object of interest is compared with the surface associated with 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 the RADARSAT-2 satellite over numerous regions of Canada. Our experimental results demonstrate that the proposed approach is robust and effective.