An extraction method for sea ice based on improved DeepLabV3+ model：Taking the Arctic Greenland Sea as an example

Sun Shichang; Wang Zhiyong; Li Zhenjin; Zhang Baojing; Tian Kang; Zhao Xiangyu

doi:10.12284/hyxb2024075

Volume 46 Issue 8

Sep. 2024

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Sun Shichang，Wang Zhiyong，Li Zhenjin, et al. An extraction method for sea ice based on improved DeepLabV3+ model：Taking the Arctic Greenland Sea as an example[J]. Haiyang Xuebao，2024, 46(8)：131–142 doi: 10.12284/hyxb2024075

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An extraction method for sea ice based on improved DeepLabV3+ model：Taking the Arctic Greenland Sea as an example

doi: 10.12284/hyxb2024075

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
National Demonstration Center for Experimental Surveying and Mapping Education (Shandong University of Science and Technology), Qingdao 266590, China

Received Date: 2023-12-12
Rev Recd Date: 2024-06-05

Available Online: 2024-08-09

Publish Date: 2024-09-26

Abstract

Abstract

Sea ice is an indicator of global climate change, and the change of Arctic sea ice is related to global warming and sea level rise. Aiming at the problems such as inaccuracy and slow speed of extracting details from sea ice by traditional semantic segmentation model, an improved DeepLabV3+ sea ice extraction method was constructed. Firstly, we replaced the Xception backbone network with MobileNetV2, which significantly reduces the network’s parameter count and save time while maintaining the accuracy of sea ice extraction. Secondly, we enhanced the ASPP module to DenseASPP, further expanding the receptive field during multi-scale feature extraction for sea ice, resulting in denser features. Lastly, we introduced a coordinate attention mechanism to strengthen the focus on both channel and spatial features, enhancing the extraction of fine edge details in sea ice. The Greenland Sea in the Arctic is selected as the experimental area, and 10 Sentinel-1A dual-polarization SAR images from the winter of 2020 to 2022 in the sea area are processed and labeled to form a data set for the experiment, we compared our method with classic models such as U-Net, PSPNet and DeepLabV3+. The results showed that our method achieved anmIoU of 88.46% and an mPA of 94.16%. Compared to the traditional DeepLabV3+, mIoU increased by 2.35%, mPA increased by 2.90%, and the parameter count and GFLOPs decreased 45.08 M and 106.01 G, respectively. Meanwhile, the training time and sea ice extraction time decreased by 68% and 30%, respectively. Compared to U-Net、PSPNet and other models, the optimal results are also obtained. Compared with other models, the new model constructed in this paper has a stronger learning ability about sea ice characteristics, can obtain more detailed information of sea ice and greatly saves time, and can provide technical support for the study of sea ice degradation monitoring under global warming environment.
- sea ice extraction,
- deep learning,
- MobileNetV2,
- DenseASPP,
- coordinate attention

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References(24)

References

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