Performance Evaluation of Interest Point Detectors for Heterologous Image Matching

Abstract

In point-based heterologous image matching algorithms, high-quality interest point detection directly affects the final image matching quality. In this paper, starting from the detection mechanism of each interest point detector, optical images and SAR images with different resolutions and covering different areas are selected as experimental data. The five state-of-the-art SAR-Harris, UND-Harris, Har-DoG, Harris-Laplace and DoG interest point detectors are analyzed in terms of scale difference adaptability, nonlinear intensity difference adaptability, distribution uniformity, image registration alignment performance and detection efficiency. Then, we performed registration experiments on images from different sensors, at different times, and at different resolutions to further validate our evaluation results. Finally, the applicable image types of each detector are summarized. The experimental results show that SAR-Harris has the best performance in scale difference adaptability, and UND-Harris has the weakest performance. In terms of nonlinear intensity difference adaptability, SAR-Harris and UND-Harris are comparable, and DoG performance is the weakest. The distribution uniformity of UND-Harris is significantly better than other detectors. Although Har-DoG is weaker than Har-Lap and DoG in repeatability, it is better than both in final image alignment performance. DoG is superior in detection efficiency, followed by SAR-Harris. A comprehensive evaluation and a large amount of experimental data are used to evaluate and summarize each detector in detail. This paper provides a useful guide for the selection of interest point detectors during heterologous image matching.