关键词: 极化合成孔径雷达, 极化白化滤波器, 胶囊网络, PolSAR图像配准, 极化协方差矩阵

Abstract: Polarimetric synthetic aperture radar (PolSAR) image registration has a wide range of applications in feature classification, change detection, and image fusion. Existing PolSAR image registration methods, whether based on deep learning or conventional methods, use PolSAR magnitude image information for processing. This processing leads to a large amount of polarization information loss, and at the same time, the registration accuracy and reliability perform poorly under the influence of the inherent coherent speckle noise of PolSAR images. To this end, this paper first develops a novel and effective key point detector based on polarization whitening filter (PWF) refinement processing, which uses PWF to suppress coherent speckle noise in PolSAR images and selects significant and uniformly distributed matching key points by threshold constraint, morphological erosion, and non-extreme value suppression. Further, in this paper, we design a Siamese simple dense capsule network (SimSD-CapsuleNet) to quickly extract the shallow texture features and deep semantic features of the data, and we use the polarization covariance matrix as the input data in order to make full use of the polarization information. In this paper, the distances between the capsule form feature descriptors are calculated and fed into a hard L2 loss function for the training of the model. The method in this paper is validated on PolSAR images acquired by different sensors with different resolutions, and the results show that the method can acquire more uniform and a larger number of matching key points in a shorter time, and the combination of PWF and deep neural network can achieve fast and accurate PolSAR image registration.

Key words: polarimetric synthetic aperture radar, PWF, CapsuleNet, PolSAR image registration, polarization covariance matrix