Robust Registration of Multimodal Remote Sensing Images Using Self-Similar Adjacent Self-Convolutional Feature

Due to significant nonlinear radiometric differences (NRD) and severe geometric distortions (e.g., translation, scale, and rotation) among multimodal images (e.g., optical, infrared, SAR), image registration remains a challenging problem. This article proposes a robust matching method based on self-similarity adjacent self-convolution features (SASF), consisting of two key steps. In the first stage, a uniform distribution of feature points in multimodal images is extracted using a FAST feature extraction operator based on normalized filtering, and geometric correction is performed using prior reference geographic location information of remote sensing images to achieve coarse registration. In the second stage, addressing the significant NRD, a novel structural feature SASF is constructed. Based on this, a rapid structural similarity measurement method ($\text{Fast}-\text{SSIM}_{\text{SASF}}$) is proposed, which utilizes 3-D fast Fourier transform techniques to enhance image matching efficiency. The proposed SASF algorithm is evaluated for performance on multiple sets of multimodal remote sensing images. Experimental results demonstrate that this method outperforms many existing excellent algorithms in terms of matching effectiveness. Finally, the SASF algorithm is tested on four pairs of entire-scene multimodal remote sensing satellite images, demonstrating its robustness to NRDs and geometric distortions.