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Using contour loss constraining residual attention U-net on optical remote sensing interpretation

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Abstract

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased \(1\%\) to \(2\%\) when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness.

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Data availability

The Mnih Massachusetts Building Dataset used or analyzed during the current study are available from the corresponding author on reasonable request. Another Construction Disturbance Dataset is not publicly available until permission is granted by the provider.

Code availability

The relevant codes are available.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2019YFE0196600), the National Natural Science Foundation of China (62072360, 61902292, 62001357, 62072359, 62172438), the key research and development plan of Shaanxi Province (2019ZDLGY13-07, 2019ZDLGY13-04, 2020JQ-844), the Natural Science Foundation of Guangdong Province of China (2022A1515010988), the Xi’an Science and Technology Plan (20RGZN0005), and the Xi’ an Key Laboratory of Mobile Edge Computing and Security (201805052-ZD3CG36).

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Authors and Affiliations

  1. The School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Peiqi Yang & Mingjun Wang

  2. The School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Hao Yuan

  3. Science and Technology on Communication Networks Laboratory, Shijiazhuang, 050000, China

    Ci He

  4. The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050000, China

    Ci He

  5. State Grid JiLin Province Electric Power Company Limited Information Communication Company, Changchun, 130000, China

    Li Cong

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  1. Peiqi Yang
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  3. Hao Yuan
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  5. Li Cong
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Contributions

PY performed the data analyses and wrote the manuscript. MW helped perform the analysis with constructive discussions. HY contributed to the conception of the study. CH contributed to analysis and manuscript preparation. LC contributed to analysis and manuscript review.

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Correspondence to Hao Yuan.

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Yang, P., Wang, M., Yuan, H. et al. Using contour loss constraining residual attention U-net on optical remote sensing interpretation. Vis Comput 39, 4279–4291 (2023). https://doi.org/10.1007/s00371-022-02590-3

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