Application of gradient based image segmentation to SAR imagery

In the current paper, we explore the use of a gradient based region growing method to segment Synthetic Aperture radar (SAR) imagery into disjoint regions and later categorize them into one of several known classes. Segment based features, as opposed to pixel based features, are utilized in the supervised classification stage. The method is tested on SAR images acquired from RADARSAT-2 and AIRSAR sensors. Here, SAR amplitude data and/or features from Pauli, Cloude-Pottier decompositions are used for segmentation and classification. Qualitative and quantitative results show good identification of open-water, terrain and agricultural areas.