Cultivated land extraction from high-resolution remote sensing images based on BECU-Net model with edge enhancement

Cultivated land cover, as an important technical index to reflect the dynamic changes of human activities and the utilization degree of land resources, has been widely utilized in the fields of food security assessment and land management decision making. Existing information extraction methods ignore the differential characteristics of the plots and the rich information found in edge details, which results in fragmented extraction results with fuzzy boundaries. Therefore, an improved model that couples semantic segmentation model and edge enhancement is proposed to better solve the problem of insufficient fitting of cultivated land edges and fully utilize the rich semantic features and edge information in remote sensing images. The edge loss is designed accordingly to further improve the training accuracy and model performance. We design an edge branching self-network formed by CoT unit, gated convolution, and SCSE attention mechanism to realize the information complementarity of edge and depth features. We construct a joint edge enhancement loss function called BE-loss with constraints to enhance the attention of the model to boundary information. On this basis, we construct a cultivated land information extraction model, that is, BECU-net, by combining the EfficientNet backbone network and U-frame. In the multi-feature input layer of this model, the index and texture features of the preprocessed data are pre-extracted, the input structure is adjusted, and the feature expression ability of the network is improved. The extraction accuracy of cultivated land is 94.13%, and the F1-score is 95.17%. Compared with PANet, the extraction accuracy increased by 15.01%, and the F1-score improved by 7.93%. Compared with DeeplabV3+ network, the extraction accuracy is enhanced by 2.03%, and the F1-score is increased by 1.15%. The edge of cultivated land extracted by BECU-Net model is clear, and it is close to the real edge shape of cultivated land. Few holes and islands are observed. The extracted large parcels are not missing, and the edges and corners are sharp. The extracted small parcels have clear outlines and small deformation. At various gaps and complex edges, the extraction effect of GID dataset is significantly improved compared with that of the five other models. The effect is significant when used for edge extraction, The sawtooth and cavity phenomena of cultivated land patches are effectively restrained as well. (1) The input layer of network structure with multiple features, including exponential features and texture features, can effectively reflect the characteristics of cultivated land. (2) The edge branch subnetwork focuses on processing the shape information to better identify the boundary details in the cultivated land image. Its edge features complement the depth features of the Efficient encoder, and they can be cascaded to fully utilize the shallow details. (3) The improved combined loss function called BE-Loss with regular term solves the problem of unbalanced training sample categories and non-edge pixel-dominated loss function. Overall, the algorithm in this study provides a technical reference for further solving the problem of fuzzy boundaries when extracting cultivated land information. It also offers theoretical support for the accurate division of complex boundaries. © 2023 Science Press. All rights reserved.