TITLE:
Research on PolSAR Image Classification Method Based on Vision Transformer Considering Local Information
AUTHORS:
Mingxia Zhang, Aichun Wang, Xiaozheng Du, Xinmeng Wang, Yu Wu
KEYWORDS:
Vision Transformer, PolSAR, Image Classification, LIViT
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.9,
September
10,
2024
ABSTRACT: In response to the problem of inadequate utilization of local information in PolSAR image classification using Vision Transformer in existing studies, this paper proposes a Vision Transformer method considering local information, LIViT. The method replaces image patch sequence with polarimetric feature sequence in the feature embedding, and uses convolution for mapping to preserve image spatial detail information. On the other hand, the addition of the wavelet transform branch enables the network to pay more attention to the shape and edge information of the feature target and improves the extraction of local edge information. The results in Wuhan, China and Flevoland, Netherlands show that considering local information when using Vision Transformer for PolSAR image classification effectively improves the image classification accuracy and shows better advantages in PolSAR image classification.