Multimodal Remote Sensing Image Registration Based on Adaptive Spectrum Congruency

Multimodal remote sensing images (MRSIs) have extensive nonlinear radiation differences, geometric distortions, and noise corruption, which bring challenges for registration. Existing feature matching methods usually use gradient or phase congruency (PC) to extract image features. However, gradient and PC are sensitive to strong nonlinear radiation distortions and noises when dealing with MRSIs. To solve this problem, we propose a novel efficient feature detector called adaptive spectrum congruency (ASC). The ASC is a data-driven antinoise edge detector which adopts an adaptive threshold for noise compensation. Compared with gradient and PC, ASC is more robust to significant radiometric distortions and noise in MRSIs. Based on ASC, we develop a feature matching method for MRSIs registration. First, we propose a novel corner detection function by combining ASC and the Sobel operator to improve the repeatability of feature points. Then, we use the local histogram of ASC to construct the feature descriptor (LHASC) to describe the attributes of the feature points. LHASC is built on the ASC structural map, which can improve the discriminability and robustness of the structural descriptor. We perform extensive experiments on a variety of MRSIs to demonstrate the noise resistance and rotation invariance of our registration method. Compared with the classic and state-of-the-art methods, our method improves the average number of correct matches by at least 1.56 times, the average ratio of corrected number by 15.62%, the average success rate by 58.21% and with an average root mean square error of 1.47 pixels.