PolSAR Image Registration Combining Siamese Multiscale Attention Network and Joint Filter

Abstract

Polarimetric synthetic aperture radar (PolSAR) is an active microwave imaging system. Due to the coherence characteristic of PolSAR imaging, inherent coherent speckle noise exists in PolSAR images. The registration of PolSAR images is severely affected by speckle noise. Therefore, we first propose a joint filter that combines Refined-Lee filtering and polarimetric whitening filtering (PWF). The filter first applies Refined-Lee filtering to PolSAR images, which greatly reduces the speckle noise while maintaining high-resolution detailed information of the texture, and then uses PWF to normalize and whiten the polarimetric matrix to further limit the interference of speckle noise. After that, the binary robust invariant scalable keypoints (BRISK) algorithm is used to extract high-quality keypoints from the denoised PolSAR image. Then a novel Siamese Multiscale Attention Network (SMAN) is designed, which uses attention modules to construct feature descriptors with different scales. To fully utilize polarimetric information, we adopt the polarimetric covariance matrix and three polarimetric features as inputs to the network and the Second Order Similarity (SOS) as the loss function to train the network. In the keypoint matching stage, we present to use the symmetric displacement distance to further constrain the keypoint pairs obtained by the initial matching, which improves the accuracy of matching keypoint pairs. Experimental results show that our proposed method can effectively reduce the interference of speckle noise and overcome non-linear differences, geometric distortions, and differences in polarimetric scattering information to achieve accurate PolSAR image registration.