Small celestial body image feature matching method based on PCA-SIFT
Tianyuan Tao, Kang Zhiwei, Jin Liu, Xin James He · 2015
To improve the accuracy of image matching, a small celestial body image feature matching method based on PCA-SIFT is proposed. Firstly, the PCA-SIFT (principal component analysis-scale invariant feature transform) is utilized to extract image interest points. And then, the correlation coefficient is used as similarity measurement, which can filter image interest points. By this method, the image matching pairs can be obtained. Finally, the RANSAC (random sample consensus) algorithm is used to eliminate wrong and repeated matching pairs. The simulation results demonstrate that the proposed method improves the precision of image matching greatly. The spacecraft using this method can land more safely on the surface of small celestial body than that of traditional method.