Optical and SAR Image Registration Based on Feature Decoupling Network

Abstract

Automatic registration of optical and synthetic aperture radar (SAR) images is one of the most challenging tasks due to the influence of speckle noise and nonlinear radiation differences. In this article, we propose a compound registration method for optical and SAR images based on feature decoupling network (FDNet), which consists of a residual denoising network (RDNet) and a pseudo-Siamese fully convolutional network (PSFCN). First, we propose a fast compound matching algorithm, which can overcome the respective weaknesses of the registration accuracy and computational complexity of the feature-based and area-based methods. Specifically, FAST keypoint detection is used to generate the center of the initial template. The extraction of local feature descriptors and the matching of the initial templates are implemented by PSFCN. Second, we design an RDNet to learn the statistical model of speckle noise in SAR images and define a new loss function based on mean-square error (mse) and total variation (TV) to achieve the propagation of speckle noise. PSFCN and RDNet are used to learn deep representations of semantic and noise information, respectively. Then, the semantic and noise features are decoupled on spaced convolutional layers. Finally, the optimal matching templates are searched in a small search window around the initial matching templates. In addition, we propose a strategy for adaptively selecting the template size based on 2-D entropy, which can select the appropriate template size according to the content richness of SAR images. Registration results on a public registration dataset show that our proposed method achieves better performance than other state-of-the-art methods.

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