Performance Evaluation of Interest Point Detectors for Remote Sensing Image Matching

Interest point detection is a crucial step for the image matching because it directly influences the matching results. In order to establish the criterion of selecting the interest point detectors for remote sensing image matching, different remote sensing images in terms of spetrum, time and scale were selected to evaluate the four famous interest point detectors including Harris-Laplace, Hessian-Laplace, Difference of Gaussian (DoG) and Maximally Stable Extremal Regions (MSER), and the repeatability was used as the evaluation criterion. The merits, the demerits and application of these detectors were also discussed. The experimental results show that for spectrum and time variation, the repeatability of Hessian-Laplace detector achieves 40% whichperforms best, followed by Harris-Laplace and DoG, whereas MSER performs worst. For image scale changes, MSER outperforms other detectors and its repeatability is 35%, followed by Hession-Laplace, whereas Harris-Laplace and DoG perform worse than other detectors.