Lunar Image Matching Based on FAST Features with Adaptive Threshold

Abstract

The contrast of lunar images is low, and few features can be extracted. Therefore, lunar images can be hardly matched with high accuracy. A lunar image matching method based on features from accelerated segment test (FAST) feature and speeded-up robust features (SURFs) descriptor is presented. First, entropy of image is adopted to automatically compute threshold for extracting FAST features. Second, SURF descriptors are used to describe candidate features, and then initial matches with nearest neighborhood strategy are obtained. Third, outliers are rejected from initial matches by RANSAC-based model estimation strategy and homography constraint. Experimental results show that the proposed method can get enough image correspondences and the matching errors are less than 0.2 pixels. It indicates that the proposed method can automatically achieve high-accuracy lunar image matching and lay good foundation for subsequent lunar image stitching and fusion.