Attention-based U-Net for building extraction from remote sensing images

Qiong Zhang

doi:10.1117/12.2636788

29 April 2022 Attention-based U-Net for building extraction from remote sensing images

Qiong Zhang

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122470R (2022) https://doi.org/10.1117/12.2636788
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

To extract ground objects from remote sensing images based on deep learning method is one of the current research hotspots, and building information has attracted much attention as an important artificial feature. Convolutional neural networks have shown great potential for building extraction tasks. In view of the current research status, this paper proposes a lightweight improved network ECAU-Net that combines semantic segmentation network U-Net and efficient channel attention mechanism, and applies it to automatic extraction of buildings from high-resolution remote sensing images. Experiments show that the extraction method proposed in this paper shows good extraction results on the Massachusetts building data set, and the accuracy, recall, precision, and F1 score are better than the original U-Net. Therefore, this method does have certain advantages.

Citation Download Citation

Qiong Zhang "Attention-based U-Net for building extraction from remote sensing images", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122470R (29 April 2022); https://doi.org/10.1117/12.2636788

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KEYWORDS

Remote sensing

Feature extraction

Image segmentation

Convolution

Neural networks

Visualization

Network architectures

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