Research on a Classification Method of Crop Planting Structures Based on Indexes and an Improved U-Net Network
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This paper proposes a method for extracting crop planting structures based on indexes, image fusions and an improved U-Net deep semantic segmentation network; this method can automatically and accurately extract multiple types of information regarding crop planting structures. Taking the Landsat 8 commercial multispectral satellite dataset as an example, three indexes are used to highlight the characteristics of vegetation, water and soil. The GS method is used to combine the indexes to fuse panchromatic bands and obtain 15-m high-resolution fused images, and the images are sliced to obtain training sets and test sets of images. When using different combinations of index-fused images and the improved U-Net network to classify the test set data, the overall accuracy rate reaches more than 92.4%, and the accuracy rate of the crop planting structure (wheat, cotton, and woodland) reaches up to 94.2%. The results showed that the participation of AWEI in the image synthesis process can significantly improve the classification effect of the crop planting structure by more than 1.2%, and the kappa coefficient of the NDVI-AWEI method reaches 0.886; thus, this method is the most stable and suitable for actual production. This method can assist relevant enterprises in adjusting and optimizing their regional planting structures, in the intelligent allocation of irrigation water and in other related decisions.