Supervised Hyperspectral Remote Sensing Image Classification Using Graph Attention Networks with Hybrid Pooling

Abstract

Traditional remote sensing image surveys rely on field observations, which have shortcomings such as low efficiency and limited coverage, making it difficult to meet the needs of urban planning and management. In response to this, this article conducts in-depth research on remote sensing image classification methods based on graph neural networks (GNN). This study adopts a joint framework of GAT (Graph Attention Network)+SLC (Simple Linear Iterative Clustering)+Hybrid Pooling. Use SLIC algorithm to perform superpixel segmentation on hyperspectral data and construct a graph structure that integrates spectral spatial features. Utilizing GAT's attention mechanism to enhance feature interaction among neighboring nodes, combined with a hybrid pooling strategy to balance feature dimensionality reduction and key information preservation. The experiment was conducted on the public datasets WHU-RS19 and UC Merced, using traditional CNN, random forest, and minimum distance methods as baseline models. The significance of the differences was verified through independent sample t-test. By expanding the sample coverage, optimizing the model regularization strategy, and continuously iterating training, the generalization performance of the model has been significantly improved. Therefore, from the comparison of classification performance, graph NN classification is superior to minimum distance classification. This method utilizes perspective remote sensing data to achieve accurate ground target classification and dynamic change information extraction, providing reliable data support for urban land survey and management.