Segmenting Shadows from Synthetic Aperture Radar Imagery Using Edge-Enhanced Region Growing

摘要

An enhanced rgion-growing approach for segmenting regions is introduced. A region-growing algorithm s merged with stopping criteria based on a robust noise-tolerant edge-detection routine. The region-grow algorithm is then used to segment the shadow region in a Synthetic Aperture Radar (SAR) image. This approach recognizes that SAR phenomenology causes speckle in imagery even to the sharow area due to energy injected from the surrounding cluttr and target. The speckled image makes determination of edges a difficult task even for the human observer. This paper outlines the edge-enhanced rgion grow approach and compares the results to three other segmentation approaches including the rgion-grow only approach, an automated-threshold approach based on a priori knowledge of the SR target information, and the manual segmentation approach. The comparison is shown using a tri-metric inter-algorithmic approach. The metrics used to evaluate the segmentation include percent-pixels same (PPS). the partial-dircted hausdorff (PDH) metric, and a shape-based metric based on the complex inner product (CIP), Experimental results indicate that the enhanced region-growing technique is a reasonable segmentation for the SAR trrget image chips obtaiend from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program.