Rank-Based Local Self-Similarity Descriptor for Optical-to-SAR Image Matching

• Published in: IEEE geoscience and remote sensing letters Vol. 17; no. 10; pp. 1742 - 1746
• Main Authors: Xiong, Xin, Xu, Qing, Jin, Guowang, Zhang, Hongmin, Gao, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive optics; Correlation; Correlation coefficient; Correlation coefficients; Histograms; Image matching; local self-similarity (LSS); Nonlinear optics; Optical imaging; Optical sensors; optical-to-synthetic aperture radar (SAR); Radar imaging; Radiometry; rank; SAR (radar); Self-similarity; Statistical methods; Synthetic aperture radar; Template matching
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2019.2955153

Automatic optical-to-synthetic aperture radar (SAR) image matching is still a challenging task due to the existence of severe nonlinear radiometric differences between the images and the presence of strong speckles in the SAR images. To address this problem, we propose a novel feature descriptor called rank-based local self-similarity (RLSS) for optical-to-SAR image template matching. The RLSS descriptor is an improved version of the local self-similarity (LSS) descriptor, inspired by Spearman's rank correlation coefficient in statistics. It can describe the local shape properties of an image in a discriminable manner. To further improve the discriminability, a dense RLSS (DRLSS) descriptor is formed with a dense scheme by integrating the RLSS descriptors for multiple local regions into a dense sampling grid. Experimental results conducted based on the optical and SAR image pairs demonstrated that the proposed descriptor was robust to nonlinear radiometric differences and it outperformed two state-of-the-art descriptors [dense LSS (DLSS) and histogram of orientated phase congruency (HOPC)].