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Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock module inside the Resnet34 network to obtain the SEResnet34 network. Finally, the Dual Attention Network (DANet) is added outside the SEResnet34 network to enable the model to adaptively enhance the perception of water body parts. In the Decode section, upsampling is performed by bilinear interpolation and fused with the feature extraction layer layer by layer to finally obtain the segmented image for water body extraction. (3) SER34AUnet model training: Firstly, the established dataset is divided into training and test sets in the ratio of 8:2. Next, the FCN_Resnet50, DeeplabV3_Resnet50, Unet semantic segmentation network model and the improved model in this paper are trained and tested. Finally, the visual and numerical analyses of the two lake water segmentation results and the three evaluation indexes of the test set, respectively, demonstrate the good applicability of the method proposed in this paper for lake water extraction in cold and arid regions.
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