High-resolution Remote Sensing Image Semantic Segmentation Based on GLNet and HRNet

The backbone of a convolutional neural network global branch, a residual network (ResNet), obtains low-resolution feature maps at side outputs that lack feature representation. The local branch aggregates the feature maps in the global branch, which are not fully learned, resulting in a negative impact on image segmentation. To solve these problems in GLNet (Global-Local Network), a new semantic segmentation network based on GLNet and High-Resolution Network (HRNet) is proposed. First, we replaced the original backbone of the global branch with HRNet to obtain high-level feature maps with stronger representation. Second, the loss calculation method was modified using a multi-loss function, causing the outputs of the global branch to become more similar to the ground truth. Finally, the local branch was trained independently to eliminate the confusion produced by the global branch. The improved network was trained and tested on the remote sensing image dataset. The results show that the mean absolute errors of the global and local branches are 0.0630 and 0.0479, respectively, and the improved network outperforms GLNet in terms of segmentation accuracy and mean absolute errors.