Establish keypoint matches on multispectral images utilizing descriptor and global information over entire image

This paper proposes an approach to registering multispectral images by establishing keypoint matches. The matching ability of descriptors is characterized by the repeatability and distinctiveness that typically decrease on multispectral images. The decrease of matching ability often yields a set of keypoint matches containing a high rate of incorrect matches, and in this case the outlier matches are very difficult to be removed. To establish reliable keypoint matches, this paper proposes an approach of two stages. Firstly, keypoint matches of smaller descriptor distance are obtained as an initial set. Secondly, complementary information to the local window for computing descriptors is employed to evaluate keypoint matches and find good matches. A smaller descriptor distance for a keypoint match implies a greater probability of being correct and hence the initial set contains a higher rate of correct matches. The global information can be viewed as a means of enhancing the matching ability of descriptors, compensating the decrease of common information between multispectral images. Experimental results show that the proposed method can effectively establish keypoint matches on multispectral images of large spectral difference.