Combining Phase Congruency and Self-Similarity Features for Multimodal Remote Sensing Image Matching

Abstract

The structural features using self-similarity have become more popular for multimodal remote sensing image matching. However, mostly because of significant geometric distortions and nonlinear intensity differences between images, these methods produce a limited matching performance when directly applied to multimodal remote sensing images. To address that, we propose a novel feature descriptor named pyramid features of orientated self-similarity (POSS) for multimodal remote sensing image matching, which integrates phase congruency (PC) into the self-similarity model for better encoding structural information. Unlike these conventional self-similarity-based descriptors, the POSS is constructed by using PC instead of image intensity in a pyramid manner and thus has better discriminability and robustness to modality variations. In addition, we design a uniform multimoment of PC (UMMPC) detector for feature detection, which can improve the number, distribution, and repeatability of feature points. Experimental results demonstrate its superior applicability of the proposed method over the state-of-the-art methods, showing much better matching performance.