关键词: 撞击坑检测, 鲁棒主成分分析, 视觉显著性, 撞击坑候选区域

Abstract:

Crater is important for analyzing the relative dating of planetary and lunar surfaces. For the complex terrains in remote sensing images, a robust blocked principal components analysis (RPCA) approach was proposed to automatically detect crater candidate regions. An alternating direction multipliers algorithm was presented for RPCA based on the blocked planetary images. The background is modeled as a low-rank matrix, and the salient regions map is represented by structure sparse parts that contain potential craters. The crater candidates are obtained by mathematical morphological operations for the saliency regions map, they are precisely distinguished from falsely detected ones through a sparse representation classifier in feature space. Experiments on the images from Mars and Moon demonstrate show that the accuracy rate of crater recognition can reach up to 91.7% by effectively eliminating the effects of background and illumination.

Key words: crater detection, robust principal components analysis, visual saliency, crater candidate blocks