BFETNet: a bitemporal feature enhancement transformer network for remote sensing image change detection

Boyu Yang; Jingxuan Liu; Zijian Zhang; Kangle Liu; Hongming Zhang

doi:10.1117/12.3037728

22 October 2024 BFETNet: a bitemporal feature enhancement transformer network for remote sensing image change detection

Boyu Yang, Jingxuan Liu, Zijian Zhang, Kangle Liu, Hongming Zhang

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Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740F (2024) https://doi.org/10.1117/12.3037728
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

Remote sensing (RS) image change detection (CD) plays a crucial role in monitoring and understanding dynamic environmental processes. In recent years, CD tasks have seen numerous attempts involving pure convolutional neural networks (CNNs). However, it has become evident that CNNs are limited in their capacity to capture global context and long-range spatial relationships. In this paper, we propose a Bitemporal Feature Enhancement Transformer Network (BFETNet) for remote sensing image change detection. Specifically, the BFENet utilizes a transformer encoder-decoder network to enrich the contextual information of CNN features by incorporating a designed spatial-channel semantic tokenizer (SCST). Besides, we employ a channel-coordinate attention module (CCAM) to further model the positional information and channel information of the feature map. Finally, the change map is obtained by taking the difference of the two feature maps. Extensive experiment results on both LEVIR-CD and WHU-CD datasets show that the BFETNet performs significantly better than the existing state-of-the-art CD methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Boyu Yang, Jingxuan Liu, Zijian Zhang, Kangle Liu, and Hongming Zhang "BFETNet: a bitemporal feature enhancement transformer network for remote sensing image change detection", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740F (22 October 2024); https://doi.org/10.1117/12.3037728

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KEYWORDS

Transformers

Remote sensing

Feature extraction

Semantics

Image enhancement

Education and training

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