VHR imagery change detection using UNet based on fusion of residual polymer and attention mechanism

Mengxuan Zhang; Xiangbing Kong; Junjie Chen; Hong Zang; Kai Guo; Yinan Wang

doi:10.1117/12.2626443

21 December 2021 VHR imagery change detection using UNet based on fusion of residual polymer and attention mechanism

Mengxuan Zhang, Xiangbing Kong, Junjie Chen, Hong Zang, Kai Guo, Yinan Wang

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560H (2021) https://doi.org/10.1117/12.2626443
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

VHR imagery change detection is one of research hotspots and difficulties in the field of remote sensing. However, the traditional remote sensing image change detection method is a waste of time and energy and low efficiency. In recent years, deep learning approaches in remote sensing image change detection verified feasible and save time to improve efficiency. A UNet change detection method based on aggregation residuals and attention mechanism is proposed, using prior knowledge of deep learning. The UNet model is used as the basic model, and the aggregation residual module is introduced in the up-down sampling stage, which can fully extract the feature information of the image. The weight of each component in the feature graph can be adjusted by adding attention module in the jump connection layer. In the process of experiment based on the model parameters are reasonable and effective set of data sets to Longnan remote sensing image change detection, and the experimental results showing that compared with the traditional deep learning semantic segmentation method, this article methods F1 value of 0.873, the generated change detection figure closer to label figure, higher accuracy, shorter.

Citation Download Citation

Mengxuan Zhang, Xiangbing Kong, Junjie Chen, Hong Zang, Kai Guo, and Yinan Wang "VHR imagery change detection using UNet based on fusion of residual polymer and attention mechanism", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560H (21 December 2021); https://doi.org/10.1117/12.2626443

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KEYWORDS

Remote sensing

Image segmentation

Data modeling

Image processing

Convolution

Feature extraction

Neural networks

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