Robust Registration of Multimodal Remote Sensing Images With Spectrum Congruency

Abstract

Among the existing registration methods, most feature descriptors are designed with image intensity, gradient information and phase congruency (PC). However, both intensity and gradient are sensitive to image illumination changes, complex intensity differences, noise, etc. Despite the fact that PC is invariant to image illumination and contrast, it does not perform well when images are corrupted with noise and nonlinear radiation distortions. In this article, we propose a novel feature called spectrum congruency (SC), which is robust to noise and variations of image illumination and intensity. SC focuses on exploiting the correlation of the multiscale patches based on their local energy and measures the congruency of the energy distribution in a data-driven transform domain. To demonstrate the superiority of SC, we apply it to multimodal image registration. We construct a histogram-based feature descriptor based on SC, termed as HOSC. Then the HOSC descriptor is integrated with two similarity metrics for multimodal remote sensing image registration. Extensive experimental results on both real and noisy image pairs show that the proposed method presents superior registration accuracy and excellent performance in resisting the nonlinear distortion and noise.