Graph Attention Guidance Network With Knowledge Distillation for Semantic Segmentation of Remote Sensing Images

Deep learning has become a popular method for studying the semantic segmentation of high-resolution remote sensing images (HRRSIs). Existing methods have adopted convolutional neural networks (CNNs) to achieve better segmentation accuracy of HRRSIs, and the success of these models often depends on the model complexity and parameter quantity. However, the deployment of these models on equipment with limited resources is a significant challenge. To solve this problem, a lightweight student network framework-a graph attention guidance network (GAGNet) with knowledge distillation (KD), called GAGNet-S*-is proposed in this study, which distills knowledge from pretrained large teacher network (GAGNet-T) and builds reliable weak labels to optimize untrained student network (GAGNet-S). Inspired by the graph convolution network, this study designs a graph convolution module called the attention-graph decoder (AGD), which combines attention mechanisms with graph convolution to optimize image features and improve segmentation accuracy in the semantic segmentation task of HRRSIs. In addition, a dense cross-decoder (DCD) was designed for multiscale dense fusion, which utilizes rich semantic information in the high-level features to guide and refine the low-level features from the bottom up. Extensive experiments showed that GAGNet-S* (GAGNet-S with KD) achieved excellent segmentation performance on two widely used datasets: Potsdam and Vaihingen. The code and models are available at https://github.com/F8AoMn/GAGNet-KD.