Automatic segmentation of parallel drainage patterns supported by a graph convolution neural network

作者：

Highlights：

• GraphSAGE was applied in the segmentation of parallel drainage pattern (SPDP).

• A method of building drainage dual graph (DDG) with hydrology knowledge is proposed.

• The GraphSAGE outperforms other machine learning methods and GCNNs in SPDP.

• The knowledge-based DDG reduces the quantity of DP samples for training.

摘要

•GraphSAGE was applied in the segmentation of parallel drainage pattern (SPDP).•A method of building drainage dual graph (DDG) with hydrology knowledge is proposed.•The GraphSAGE outperforms other machine learning methods and GCNNs in SPDP.•The knowledge-based DDG reduces the quantity of DP samples for training.

论文关键词：Parallel drainage pattern,Automatic segmentation,Graph theory,Drainage features,Graph sample and aggregate

论文评审过程：Received 3 March 2022, Revised 16 August 2022, Accepted 17 August 2022, Available online 22 August 2022, Version of Record 26 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118639