An extraction method for sea ice based on improved DeepLabV3+model:Taking the Arctic Greenland Sea as an example
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 con-structed.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 en-hanced 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 anmloU of 88.46％and an mPA of 94.16％.Compared to the traditional DeepLabV3+,mloU increased by 2.35％,mPA increased by 2.90％,and the parameter count and GFLOPs decreased 45.08 M and 106.01 G,respect-ively.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 de-tailed information of sea ice and greatly saves time,and can provide technical support for the study of sea ice de-gradation monitoring under global warming environment.
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