Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (3): 450-462.doi: 10.11947/j.AGCS.2024.20230197

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PolSAR image registration combining polarization whitening filtering and SimSD-CapsuleNet

XIANG Deliang1,2, DING Huaiyue1, GUAN Dongdong3, CHENG Jianda4, SUN Xiaokun1   

  1. 1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
    2. Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
    3. College of Operational Support, Rocket Force University of Engineering, Xi'an 710025, China;
    4. School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
  • Received:2023-06-08 Revised:2023-09-11 Published:2024-04-08
  • Supported by:
    The National Natural Science Foundation of China (No. 62171015)

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

CLC Number: