SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network

To address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of di...

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Main Authors: Honghao Fu, Yilang Shen, Yuxuan Liu, Jingzhong Li, Xiang Zhang
Format: Article
Language:English
Published: Elsevier 2023-08-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843223002650
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author Honghao Fu
Yilang Shen
Yuxuan Liu
Jingzhong Li
Xiang Zhang
author_facet Honghao Fu
Yilang Shen
Yuxuan Liu
Jingzhong Li
Xiang Zhang
author_sort Honghao Fu
collection DOAJ
description To address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of digital elevation model (DEM) landform. The SGCN uses superpixel segmentation and graph convolutional networks (GCNs). Specifically, we employ the Simple Linear Iterative Clustering (SLIC) algorithm to perform superpixel segmentation on the DEM image and convert the resulting distribution of superpixels in the 2D image into a graph structure, where nodes are labeled. We then analyze the geographic features and external neighborhood relationships within each DEM superpixel to obtain corresponding node information for the graph structure. Finally, the GCN model is trained according to the graph structure, node feature information, and node label, enabling automatic recognition of node landform types. Importantly, SGCN accounts for the multi-order neighborhood features of the basic analysis unit and expresses the DEM micro-features relatively macroscopically. Experimental results demonstrate that compared with pixel-level semantic segmentation methods like U-Net, FCN, and SegNet, the SGCN significantly improves DEM landform classification accuracy by 17.84%, 33.22%, and 30.60%, respectively. Moreover, SGCN better preserves the integrity and continuity of features inside and outside the analysis area, overcoming the limitation of traditional methods that use regular grids as the basic analysis unit, which can compromise landform edge continuity.
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spelling doaj.art-904b2ce16a2143f181704b848e979f392023-08-24T04:34:20ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-08-01122103441SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional networkHonghao Fu0Yilang Shen1Yuxuan Liu2Jingzhong Li3Xiang Zhang4School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China; School of Instrument Science and Engineering, Southeast University, Nanjing 211189, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China; Corresponding author.School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, ChinaTo address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of digital elevation model (DEM) landform. The SGCN uses superpixel segmentation and graph convolutional networks (GCNs). Specifically, we employ the Simple Linear Iterative Clustering (SLIC) algorithm to perform superpixel segmentation on the DEM image and convert the resulting distribution of superpixels in the 2D image into a graph structure, where nodes are labeled. We then analyze the geographic features and external neighborhood relationships within each DEM superpixel to obtain corresponding node information for the graph structure. Finally, the GCN model is trained according to the graph structure, node feature information, and node label, enabling automatic recognition of node landform types. Importantly, SGCN accounts for the multi-order neighborhood features of the basic analysis unit and expresses the DEM micro-features relatively macroscopically. Experimental results demonstrate that compared with pixel-level semantic segmentation methods like U-Net, FCN, and SegNet, the SGCN significantly improves DEM landform classification accuracy by 17.84%, 33.22%, and 30.60%, respectively. Moreover, SGCN better preserves the integrity and continuity of features inside and outside the analysis area, overcoming the limitation of traditional methods that use regular grids as the basic analysis unit, which can compromise landform edge continuity.http://www.sciencedirect.com/science/article/pii/S1569843223002650Landform classificationDEMSuperpixelGCN
spellingShingle Honghao Fu
Yilang Shen
Yuxuan Liu
Jingzhong Li
Xiang Zhang
SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
International Journal of Applied Earth Observations and Geoinformation
Landform classification
DEM
Superpixel
GCN
title SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
title_full SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
title_fullStr SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
title_full_unstemmed SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
title_short SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
title_sort sgcn a multi order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network
topic Landform classification
DEM
Superpixel
GCN
url http://www.sciencedirect.com/science/article/pii/S1569843223002650
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AT yuxuanliu sgcnamultiorderneighborhoodfeaturefusionlandformclassificationmethodbasedonsuperpixelandgraphconvolutionalnetwork
AT jingzhongli sgcnamultiorderneighborhoodfeaturefusionlandformclassificationmethodbasedonsuperpixelandgraphconvolutionalnetwork
AT xiangzhang sgcnamultiorderneighborhoodfeaturefusionlandformclassificationmethodbasedonsuperpixelandgraphconvolutionalnetwork