LSRNet: lightweight Siamese attention remote sensing building image change detection

Jiaxing Du; Qinxin Wang; Zhijian Yin; Junya Liu; Jun Li; Zhen Yang

doi:10.1117/12.3038568

23 August 2024 LSRNet: lightweight Siamese attention remote sensing building image change detection

Jiaxing Du, Qinxin Wang, Zhijian Yin, Junya Liu, Jun Li, Zhen Yang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132500G (2024) https://doi.org/10.1117/12.3038568
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

With the rapid development of the Internet, especially the extensive application of deep learning, change detection has been successfully applied in many fields, but the pursuit of greater feature information content has led to an increase in memory and computing power requirements. To solve this problem, a Lightweight Siamese attention Residual Network (LSRNet) is proposed in this paper to reduce the memory and computational power requirements, embed the Siamese Fast Small Attention (SFSAttention) to filter out feature information with less relevance, and then use feature fusion of channel dimensions to ensure feature richness and reduce the number of parameters of the model. The residual network module is introduced to extract the entire feature information and obtain the true change graph. Under the condition of ensuring accuracy, the number of parameters in the LEVIR-CD and CCD datasets is reduced by 3.7 M and 4 M, and the number of FLOPs is reduced by 2.5 G and 2.9 G, respectively.

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

Citation Download Citation

Jiaxing Du, Qinxin Wang, Zhijian Yin, Junya Liu, Jun Li, and Zhen Yang "LSRNet: lightweight Siamese attention remote sensing building image change detection", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132500G (23 August 2024); https://doi.org/10.1117/12.3038568

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KEYWORDS

Data modeling

Remote sensing

Feature fusion

Feature extraction

Image fusion

Education and training

Image filtering

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