Optical and SAR Image Registration Using Complexity Analysis and Binary Descriptor in Suburban Areas

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Xie, Zhihua, Liu, Jinghong, Liu, Chenglong, Zuo, Yujia, Chen, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Binary descriptor; Complexity; complexity analysis; Complexity theory; Computer applications; Coordinate systems; Coordinates; Correlation; Histograms; Image edge detection; Image processing; Image registration; local self-similarity (LSS); Nonlinear optics; Optical imaging; Optical scattering; Radar imaging; Radar polarimetry; Registration; SAR (radar); Self-similarity; Suburban areas; Synthetic aperture radar
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2021.3071870

Optical and synthetic aperture radar (SAR) image registration is a challenging task due to significant geometric and radiometric differences. In particular, the strong scattering phenomenon in SAR images can seriously affect the registration results. Accordingly, to solve the low repeatability of the key points in optical and SAR images, a complexity analysis scheme is proposed. In the first phase, the complexity distribution diagram is calculated by the threshold sliding window in the edge images obtained from the maximum moment of the phase congruency. Then morphological operation and connected regions are used to obtain the high-complexity regions and mask them to avoid the extraction of the interference points. Next to solve the limitations of the local self-similarity (LSS) descriptor in the optical and SAR image registration, such as poor discrimination, expensive computational complexity, and sensitivity to large geometric and radiometric difference, we propose a binary LSS descriptor (BLSS). We replace the correlation surface of the LSS with the local gradient orientation histogram. Furthermore, we construct descriptors based on the XY -coordinate system and convert the correlation of the regions to binary descriptors. Finally, the fast sample consensus (FSC) is used to remove false correspondences. The experiments conducted on several optical and SAR image pairs verify the effectiveness of the proposed algorithm.