Graph Neural Network Classification of Large‐Format Remote Sensing Image Combining Parallel Fine Segmentation Strategy and Graph Neighborhood Relationship

ABSTRACT Although accurate classification of large‐format, high‐resolution remote sensing image is essential for land cover mapping, balancing computational efficiency with classification performance remains challenging. Traditional methods often incur high computational costs and achieve limited accuracy when applied to large‐format data. This study addressed the core challenge in the classification of large‐format and high‐resolution remote sensing images through a graph neural network classification method that combines a parallel fine segmentation strategy with graph neighborhood relationship optimization. It improved the computational efficiency bottleneck and classification accuracy. Our methodology comprises three key components. First, an adaptive compactness parameter‐based tiling method using simple linear iterative clustering (SLIC) generates uniform image patches through radiometric resolution downsampling and parallel allocation via Spark. Second, we propose a SLIC algorithm considering ground features (SLIC‐GF), which employs the Otsu method and ratio vegetation index (RVI) to distinguish vegetation/non‐vegetation pixels before fine segmentation. Finally, image objects are structured into graphs for classification via graph neural networks, with neighborhood‐based correction of misclassified nodes. Experimental results from three high‐resolution datasets show that our parallel segmentation strategy improves average computational efficiency threefold while reducing standard deviation (SD) and value range (R) by 41.5% and 51.9%, respectively. Compared to original SLIC, SLIC‐GF improves achievable segmentation accuracy (ASA) by 3.53%, reduces under‐segmentation error (UE) by 5.6%, and increases boundary recall (BR) by 2.8%. Furthermore, the graph attention network (GAT) with neighborhood optimization significantly enhances classification performance, yielding average improvements of 0.0165 in Kappa coefficient and 1.08% in overall accuracy (OA).