High-resolution remote sensing image change detection network with Transformer structure

doi:10.13474/j.cnki.11-2246.2022.0229

Abstract

Abstract: In order to solve the problem of global context information capture in remote sensing image change detection, this paper proposes TSU-Net based on twin structure, jump link structure and Transformer structure. The model encoder adopts a hybrid CNN-Transformers structure to capture the global context information of remote sensing images with the help of self-attention mechanism, which enhances the model's ability of long distance context modeling for pixel-level remote sensing image change detection task. The model is tested in the LEVIR-CD dataset and the CDD dataset, and the F1 scores are 0.9073 and 0.9314, respectively, which are superior to the comparison models.

Key words: deep learning, remote sensing image change detection, Transformer, TSU-Net

CLC Number:

P237

FENG Weiming, ZHANG Xinchang, SUN Ying, JIANG Ming, GAN Qiao, HOU Xingxing. High-resolution remote sensing image change detection network with Transformer structure[J]. Bulletin of Surveying and Mapping, 2022, 0(8): 36-40,92.

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