Zhang, L., Chen, Z., Zou, B. and Gao, Y. (2018). Polarimetric SAR Terrain Classification Using 3D Convolutional Neural Network. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 22-27 July 2018, 4551-4554.
https://doi.org/10.1109/igarss.2018.8519557

has been cited by the following article:

• 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.