An Effective Shadow Extraction Method for SAR Images
Zhiyuan Zhao, Xiaorong Xue, Yisheng Fan, Xiang Xiao · 2023
Since the target's shadow in a synthetic aperture radar (SAR) image can provide significant features, it is becoming a crucial discriminative feature for interpreting SAR images. In this paper, we propose a new segmentation method based on simple linear iterative clustering (SLIC) superpixel segmentation and merging to extract the targets' shadow regions in SAR images. The process is divided into four stages. Firstly, the original SAR image is preprocessed using logarithmic transform and anisotropic diffusion filtering. Secondly, the preprocessed image is segmented with the SLIC method. Then, we propose a technique based on the shadow superpixel marker to merge superpixels to obtain the shadow region. Finally, the merged edge of the shadow region is smoothed using the morphological closing operation to get the final shadow detection result. The experimental results based on the public Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of the proposed method.