Farmland Change Detection Algorithm Based on Improved BIT

Farmland non-agriculturalization is a serious threat to global food security and ecological stability. Remote sensing change detection technology has become a core tool for identifying the process of farmland non-agriculturalization by virtue of its advantage of large-scale dynamic monitoring. However, existing methods face challenges in balancing the extraction of fine edge details in fragmented farmland with the maintenance of global semantic consistency in large-scale fields, often resulting in blurred edges and lost local features. To address these issues, a farmland change detection algorithm based on improved BIT, named Far-CDNet, is proposed. Firstly, a detail enhancement convolution module that connects ordinary convolution and multiple differential convolutions in parallel is introduced, and the edge detail representation capability of the feature extraction network is enhanced through dynamic weighting and residual connection. Secondly, the ordinary convolution of the semantic tokenizer in the BIT module is replaced by a deep separable convolution to enhance the local feature capture ability and generate output features with higher-level semantics, so as to improve the overall feature expression ability of the model. Finally, a residual branch is added to further integrate the local and global information before and after the Transformer. The experimental results show that the improved model F1 score is 79.18%, and IoU is 69.32%. Compared with the BIT model, the F1 score is increased by 4.17%, and IoU is increased by 4.24%.