Drainage patterns recognition is essential for analyzing terrain and geomorphology,exploring geological minerals,and transforming river network data across various scales.However,traditional spatial statistical methods based on morpho-logical and geometric features are not robust enough.To overcome this deficiency,graph convolutional methods have emerged as a popular solution.Nevertheless,these methods often focus narrowly on local features,disregarding the crucial global per-spective necessary for comprehensive analysis.To address this issue,our study proposes a drainage pattern recognition method supported by graph Transformer.This method incorporates geometric knowledge by constructing river network graph struc-tures using dual graphs.It integrates a GraphSAGE-based local learning module and a Transformer-based global learning mod-ule,training the graph Transformer model.Experimental results demonstrate that our method achieves 94％accuracy in accu-rately recognizing drainage patterns by combining local segment composite features and global river network morphology fea-tures.This outperforms the 1st-ChebNet and GraphSAGE methods,presenting a promising approach for intelligent drainage pattern recognition.
维普
万方数据
