Research on the method of high-resolution remote sensing water change detection combining transformer and Siamese neural network

Fei Yi

doi:10.1117/12.2684622

21 July 2023 Research on the method of high-resolution remote sensing water change detection combining transformer and Siamese neural network

Fei Yi

Author Affiliations +

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127172S (2023) https://doi.org/10.1117/12.2684622
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Aiming at the problems of missed detection, false detection and rough edge recognition in the detection of water body changes in high-resolution remote sensing images of the same area before and after, an improved network model is proposed to better improve the extraction accuracy of water body change areas. Combining deep learning with machine vision we introduce the transformer block into the network. We design a Siamese change detection network with stronger contextual change semantic feature extraction capability SCTNet (Siam-CNN-transformer-Network), transformer block directly connects information at any location through a multi-head intention mechanism efficiently compute long sequence information mining long-distance dependencies. As the front-end of the network, the CNN model uses convolution to focus on the correlation between two-bit local data, ensuring the correlation between adjacent pixels to enhance the local modeling ability of the model and reduce the model's dependence on the amount of data. The proposed model performs well on the water body change dataset made from the WFV image data of GF-1 satellite. The experimental accuracy rate reaches 97.4%, F_1-score and mIoU 97.9% and 90.9% respectively. Compared with other change detection models, all indicators have been significantly improved, the situation of false detection and missed detection has been improved, and the effect of water edge recognition has been enhanced.

Citation Download Citation

Fei Yi "Research on the method of high-resolution remote sensing water change detection combining transformer and Siamese neural network", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127172S (21 July 2023); https://doi.org/10.1117/12.2684622

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KEYWORDS

Remote sensing

Data modeling

Transformers

Feature extraction

Neural networks

Deep learning

Image classification

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