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Research on farmland boundary extraction based on deep learning and edge detection operator for GF-2 data

With the application of agriculture in remote sensing technology, it has become a development trend to extract accurate farmland boundary information from high-resolution remote sensing images. Farmland plot is the smallest unit of crop planting, so accurate boundary extraction of farmland plot is of great significance for crop classification. Jilin Province in Northeast of China is selected as the experimental area. Taking GF-2 data as the data source, the effects of UNet, HED algorithm based on deep learning, CNN color edge extraction algorithm and traditional morphological edge detection operator in farmland boundary extraction are compared, and then a farmland boundary extraction method based on the combination of deep learning and morphological edge detection operator is proposed in this paper. Compared with using deep learning network or morphological edge detection operators alone, this method has higher peak signal-to-noise ratio and structural similarity, and better farmland edge extraction effect.