PolSAR Image Registration Using Orientated Gradients of Polarimetric Features

Although remote sensing image registration has been developing at a high speed for decades, polarimetric synthetic aperture radar (PolSAR) image registration is still a challenging task because of the presence of polarimetric scattering differences, geometric distortions, and speckle noise. Due to the lack of PolSAR image training data, the generalization performance of deep learning-based registration methods is poor and cannot fundamentally solve the problem of PolSAR image registration. In this article, we propose a novel PolSAR image registration framework that integrates feature selection, feature descriptor extraction, and template matching. First, we use structural similarity (SSIM) to select polarimetric features that are similar in structural information, thus using structural information to overcome polarimetric scattering differences. On this basis, a feature descriptor named oriented gradient of polarimetric feature (OGPF) is proposed to overcome the polarimetric scattering information difference by extracting geometric structure information using oriented gradient channels (OGCs) and 3-D Gaussian convolution. Finally, we propose to use polarimetric whitening filter (PWF) and nonmaximum suppression (NMS) to extract keypoints with significant structural information to reduce the interference of speckle noise on keypoint selection, and further propose a template downsampling strategy to reduce the complexity of template matching. The proposed method is evaluated using six pairs of PolSAR images with different scenes, and the results show that its registration performance outperforms the state-of-the-art methods.