Research on the classification of typical crops in remote sensing images by improved U-Net algorithm

Aiming at the problem of incomplete classification features of remote sensing images extracted by traditional algorithms and low accuracy of crop classification, we use drone remote sensing images as the data source and propose an improved U-Net model to classify and recognize crops such as barley, corn, etc. in the study area. In the experiment, the remote sensing image is preprocessed, and the data set is labeled and enhanced. Secondly, the algorithm is improved by deepening the U-Net network structure, introducing the SFAM module and the ASPP module, and using the multi-level and multi-scale feature aggregation pyramid method to construct an improved U-Net algorithm. Finally model training and improvement are completed. The experimental results show that the overall classification accuracy OA reaches 88.83%, and the combined ratio of MIoU reaches 0.52. Compared with the traditional U-Net model, FCN model and SegNet model, the classification index and accuracy are significantly improved.