| Summary: | Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentation in recent years, but there are few semantic segmentation algorithms for land cover. When dealing with land cover segmentation tasks, traditional semantic segmentation networks often have disadvantages such as low segmentation precision and weak generalization due to the loss of image detail information and the limitation of weight distribution. In order to achieve high-precision land cover segmentation, this article develops a multi-scale feature aggregation network. Traditional convolutional neural network downsampling procedure has problems of detail information loss and resolution degradation; to fix these problems, a multi-scale feature extraction spatial pyramid module is made to assemble regional context data from different areas. In order to address the issue of incomplete information of traditional convolutional neural networks at multiple sizes, a multi-scale feature fusion module is developed to fuse attributes from various layers and several sizes to boost segmentation accuracy. Finally, a multi-scale convolutional attention module is presented to enhance the segmentation’s attention to the target in order to address the issue that the classic convolutional neural network has low attention capacity to the building waters in land cover segmentation. Through the contrast experiment and generalization experiment, it can be clearly demonstrated that the segmentation algorithm proposed in this paper realizes the high precision segmentation of land cover.
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