Gradient Decoupling Guided Network for High-Resolution Remote Sensing Segmentation

For the semantic segmentation of remote sensing images, most existing methods focus on directly fusing unrefined low-level features with high-level features to enhance feature representation. However, these methods often neglect the potential feature entanglement within low-level features, making it challenging to accurately extract and restore spatial details. In this article, a gradient decoupling guided network (GDGNet) is proposed to alleviate this issue. The key components of GDGNet include the hybrid gradient enhancement (HGE) module, the hierarchical gradient attention (HGA) module, and the global-local context fusion (GLCF) module. First, the HGE aggregates learnable gradient convolutions to encode gradient information, enhancing the gradient features of low-level features. Then, the HGA reweights gradient decoupling masks (GDMs) to disentangle low-level features, guiding the network to focus on essential gradient regions. Finally, the GLCF fuses low- and high-level features, generating local and global contextual features and concatenating them to achieve segmentation. We conducted comparison and ablation experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets. The experimental results demonstrate the superiority of the proposed GDGNet over several state-of-the-art methods. The codes will be available at https://github.com/wangkaiwh331/GDGNet