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LIANG Feng, ZHANG Ruixiang, CHAI Yingte, CHEN Jinyong, RU Guobao, YANG Wen. A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210078
Citation: LIANG Feng, ZHANG Ruixiang, CHAI Yingte, CHEN Jinyong, RU Guobao, YANG Wen. A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210078

A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs

doi: 10.13203/j.whugis20210078
Funds:

The National Natural Science Foundation of China (61771351)

  • Received Date: 2021-02-12
    Available Online: 2022-03-31
  • Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in SAR image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. The method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Experimental results show that the proposed method can work well for the task of sea-land segmentation, the average classification accuracy and mean intersection over union(mIoU) achieve 98.18% and 96.41%, respectively.
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    Li Peng, Pu Sixun, Li Zhenhong, et al. Coastline Change Monitoring of Jiaozhou Bay from Multi-Source SAR and Optical Remote Sensing Images since 2000[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9):1485-1492
    [2] Wang D, Cui X R, Xie F Y, et al. Multi-Feature Sea-Land Segmentation Based on Pixel-Wise Learning for Optical Remote-Sensing Imagery[J]. International Journal of Remote Sensing, 2017, 38(15):4327-4347
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A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs

doi: 10.13203/j.whugis20210078
Funds:

The National Natural Science Foundation of China (61771351)

Abstract: Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in SAR image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. The method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Experimental results show that the proposed method can work well for the task of sea-land segmentation, the average classification accuracy and mean intersection over union(mIoU) achieve 98.18% and 96.41%, respectively.

LIANG Feng, ZHANG Ruixiang, CHAI Yingte, CHEN Jinyong, RU Guobao, YANG Wen. A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210078
Citation: LIANG Feng, ZHANG Ruixiang, CHAI Yingte, CHEN Jinyong, RU Guobao, YANG Wen. A Sea-Land Segmentation Method for SAR Images Using Context-aware and Edge Attention based CNNs[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210078
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