Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (1): 63-67.doi: 10.13190/j.jbupt.2016.01.011

• Papers • Previous Articles     Next Articles

A Robust Crater Detection and Recognition Method Based on Blocked Principal Components Analysis

LU An, ZHOU Dong-hua, CHEN Mao-yin   

  1. Department Automation, Tsinghua University, Beijing 100084, China
  • Received:2015-07-27 Online:2016-02-28 Published:2016-01-29

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

CLC Number: