Global-local transformer for aerial image semantic segmentation

Semantic segmentation of remote sensing images is crucial for global geographic information analysis. However, existing methods still need improvement in accurately segmenting small target regions, often resulting in missed or misclassified small targets. To address this issue, we propose a remote sensing image segmentation model Global-Local Transformer based on global-local feature fusion attention mechanism. The model employs the classical U-Net framework, with the encoder integrating convolutional neural networks and Swin-Transformer, and the decoder composed of stacked deconvolution layers for upsampling. We also introduce a Global Information Sharing module to compensate for the Swin-Transformer's limited global information extraction capability due to its sliding window approach. Experiments on the Potsdam and LoveDA datasets validate the model's effectiveness, showing improvements in mIoU and mF1 metrics on the Potsdam dataset by 1.1% and 1.1%, respectively, and on the LoveDA dataset by 1.3% and 2.1%, respectively.