Self-Similarity Features for Multimodal Remote Sensing Image Matching

Abstract

Multimodal remote sensing image matching is a challenging task because of the existence of significant radiometric differences. To address the problem, we develop a novel multimodal remote sensing image matching method based on selfsimilarity features. The offset mean filtering method is proposed first to calculate the self-similarity features fast based on the symmetry of the self-similarity. The self-similarity features are presented through a multi-channel self-similarity map (SSM) and a corresponding multi-channel symmetric SSM. On this basis, we develop the image matching method, including a feature detector named improved maximal self-dissimilarities (IMSD) and a feature descriptor named oriented self-similarity (OSS). The IMSD detector is designed by introducing the two multi-channel SSMs into the maximal self-dissimilarities (MSD) detector for feature point detection. The OSS descriptor is proposed based on the orientations of the self-similarities extracted from the multichannel SSMs. We conduct experiments with a variety of optical, synthetic aperture radar, and light detection and ranging data. Our results demonstrate the advantages of our proposed IMSD detector and OSS descriptor in comparison with state-of-the-art detectors and descriptors, respectively. The image registration results further confirm the effectiveness of the proposed method.