A semantic segmentation algorithm for remote sensing image based on improved Upernet
To address the issues of low segmentation accuracy and insufficient utilization of shallow features in existing remote sensing image semantic segmentation algorithms,a remote sensing image semantic segmentation algorithm based on improved Upernet is proposed.Initially,it introduces a split-attention network connection structure from Resnest to reconstruct the original backbone network and integrates deformable convolution to enhance the feature extraction capability for remote sensing images of different scales.Then,it designs a feature fusion module in Upernet's downsampling path,which incorporates an efficient channel attention mechanism to improve feature representation ability.Finally,it adopts a combination of cross entropy loss function and Dice Loss for training to address sample imbalance problem during training and to accelerate model convergence.Experimental results demonstrate that the improved network achieved MIoU scores of 79.38%and 74.70%on the ISPRS Potsdam and Vaihingen datasets,respectively.Compared to DeepLabV3Plus,Pspnet,FCN,and the classic Upernet algorithm,the proposed algorithm shows average improvement of 2.16%on the Potsdam dataset and 2.21%on the Vaihingen dataset.
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