A Local Distinctive Features Matching Method for Remote Sensing Images with Repetitive Patterns

A novel feature matching method for remote sensing images with repetitive patterns is proposed in this paper. Firstly, a detector, with the feature response function considering geometric distinctiveness of image pixel as well as the support region surrounding the pixel, is proposed to detect local distinctive features. Secondly, those features with higher distinctiveness are selected as seed points and matched. A matching reliability indicator is proposed to select reliable seed matches. Then, a coarse geometric transformation is computed based on the seed matches to define a corresponding search area for each feature. Finally, a feeble interest point searching strategy is adopted to find correspondence for all the features. Experimental results demonstrate that the proposed method is able to obtain much more correct matches than traditional methods, as well as the highest matching precision (around 90 percent) in the comparative evaluations for remote sensing images with highly repetitive patterns.