Robustly building keypoint mappings with global information on multispectral images
Yong Li, Hongbin Jin, Wei Qiao, Jing Jing, Hang Yu · EURASIP Journal on Advances in Signal Processing · 2015
This paper proposes an approach to robustly build keypoint mappings on multispectral images. The distinctiveness and repeatability of descriptors often decrease significantly on multispectral images and thus give unreliable keypoint mappings. To complement this decrease, global information over entire images is induced in this work to evaluate keypoint mappings. Initial keypoint mappings are established by utilizing descriptors. A pair of keypoint mappings determines a similarity transformation T , and then it is evaluated with the induced global information that is defined to be the similarity metric between the reference image and the transformed image by T . A process is utilized that iteratively considers the pairs of keypoint mappings and searches the best reference matched keypoint for every test keypoint. Experimental results show that the proposed approach can provide more reliable keypoint mappings than SIFT, ORB, FREAK, and ISS on multispectral images.