Remote Sensing Image Segmentation Method Based on Improved Swin-Unet
To solve the problems of discontinuous boundary segmentation,target misclassification and missed classification,and holes caused by the characteristics of remote sensing image data itself,such as high resolution,complex background,and uneven lighting in remote sensing image data,a remote sensing image segmentation method based on improved Swin-Unet is proposed.Firstly,an Atrous Spatial Pyramid Pooling(ASPP)module is introduced at the end of the encoder to capture multi-scale features,enhance the network's ability to obtain different scales,and fully extract contextual information;secondly,replacing the Swin Transformer Block on the decoder side with a residual Swin Transformer Block not only preserves the original information,but also alleviates the phenomenon of gradient dispersion in the model.Finally,introducing residual attention mechanism in skip connections can make the model pay more attention to important feature information,suppress invalid information,and improve the accuracy of model segmentation.After conducting experiments on a self-built dataset,the results show that the mean Intersection over Union(mIoU)of the improved network reaches 80.55％,an increase of 4.13 percentage points,proving that the improved network can effectively improve the accuracy of remote sensing image segmentation.
万方数据
维普
