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A Multi-sensor Image Matching Method Based on KAZE-HOG Features

This paper presents a multi-sensor image matching algorithm based on KAZE-HOG features. First, the multi- sensor images to be matched are preprocessed by bilateral filtering so as to remove noise. Then, a novel feature descriptor, namely KAZE-HOG is designed to extract as well as describe the feature points. Third, a nearest neighbor algorithm is used to match the obtained feature points of the images. Finally, a M-estimator sample consensus (MSAC) algorithm is utilized to remove the mismatches, so that the precise matching results can be gotten. Experimental results show that the proposed method has strong robustness, and it has good capability to resist scale and rotation transformations. As a result, it can achieve precise matching results for multi-sensor images.