Drainage pattern recognition supported by graph Transformer

doi:10.11947/j. AGCS.2024.20230582.

Abstract

Abstract:

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 morphological 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 perspective 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 structures using dual graphs. It integrates a GraphSAGE-based local learning module and a Transformer-based global learning module, training the graph Transformer model. Experimental results demonstrate that our method achieves 94% accuracy in accurately recognizing drainage patterns by combining local segment composite features and global river network morphology features. This outperforms the 1^st-ChebNet and GraphSAGE methods, presenting a promising approach for intelligent drainage pattern recognition.

Key words: drainage pattern recognition, geometric knowledge, GraphSAGE, Transformer

CLC Number:

P208

Huafei YU, Tianqi QIU, Zhe ZHOU, Chongya GONG, Tianyuan XIAO, Min YANG, Tinghua AI. Drainage pattern recognition supported by graph Transformer[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(11): 2075-2085.

Figures/Tables 11

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河网模式类型	样本数量	训练样本（约占60%）	测试样本（约占40%）
树枝状	403	253	150
扇状	401	251	150
平行状	410	260	150
骨架状	443	293	150
矩形状	401	251	150

编号	案例	方法	模式概率
编号	案例	方法	树枝状	扇状	平行状	骨架状	矩形状
（a）		1^st-Cheb Net	0.288 7	0.041 6	0.007 6	0.592 2	0.069 9
		GraphSAGE	0.297 9	0.602 7	0.016 9	0.077 6	0.004 9
		图Transformer	0.720 6	0.000 5	0.000 0	0.278 8	0.000 1
（b）		1^st-Cheb Net	0.000 9	0.170 5	0.828 5	0.000 1	0.000 0
		GraphSAGE	0.112 9	0.862 9	0.024 0	0.000 2	0.000 0
		图Transformer	0.001 2	0.927 1	0.071 6	0.000 1	0.000 0
（c）		1^st-Cheb Net	0.065 5	0.797 6	0.136 8	0.000 1	0.000 0
		GraphSAGE	0.004 7	0.539 6	0.455 6	0.000 0	0.000 0
		图Transformer	0.006 7	0.170 9	0.800 8	0.003 8	0.017 8
（d）		1^st-Cheb Net	0.001 4	0.095 5	0.851 3	0.051 6	0.000 1
		GraphSAGE	0.007 6	0.002 9	0.022 4	0.961 1	0.005 9
		图Transformer	0.000 9	0.000 0	0.000 3	0.965 2	0.033 6
（e）		1^st-Cheb Net	0.749 2	0.216 5	0.003 9	0.003 4	0.027 0
		GraphSAGE	0.004 2	0.000 0	0.000 3	0.795 9	0.199 6
		图Transformer	0.234 5	0.000 1	0.013 1	0.007 4	0.744 8