Rice extraction from Sentinel-2A image based on feature optimization and UPerNet:Swin Transformer model

Starting from the problem that rice extraction from remote sensing images still faces effective feature construction and extraction model, the feature optimization and combined deep learning model are considered. Taking Sentinel-2A image as data source, a multi-dimensional feature data set including spectral features, red edge features, vegetation index, water index and texture features is constructed. The ReliefF-RFE algorithm is used to optimize the features of the data set for rice extraction, and the combined UPerNet-Swin Transformer model is used to extract the rice from the study area based on the optimized features. Comparison with other feature combination schemes and deep learning models demonstrates that: (1) using the optimized features based on the ReliefF-RFE algorithm has the best segmentation effect for rice extraction, which its accuracy, recall rate, F1 score and IoU reach 92.77%, 92.28%, 92.52% and 86.09%, respectively, and (2) compared with PSPNet, Unet, DeepLabv3+ and the original UPerNet models, the combined UPerNet-Swin Transformer model has fewer misclassifications and omissions under the same optimal feature combination schemes, which the F1 score and IoU are increased by 11.12% and 17.46%, respectively