Multi-attribute smooth graph convolutional network for multispectral points classification  

作　　者：WANG QingWang GU YanFeng YANG Min WANG Chen 

机构地区：[1]School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China [2]Huawei Technology Co.,Ltd.,Shanghai 200120,China [3]North China Sea Marine Technical Support Center,Ministry of Natural Resources,Qingdao 266000,China

出　　处：《Science China(Technological Sciences)》2021年第11期2509-2522,共14页中国科学（技术科学英文版）

基　　金：supported by the Key Research and Development Project of Ministry of Science and Technology(Grant No.2017YFC1405100);in part by the National Natural Science Foundation of Key International Cooperation(Grant No.61720106002)。

摘　　要：Multispectral points, as a new data source containing both spectrum and spatial geometry, opens the door to three-dimensional(3D) land cover classification at a finer scale. In this paper, we model the multispectral points as a graph and propose a multiattribute smooth graph convolutional network(Ma SGCN) for multispectral points classification. We construct the spatial graph,spectral graph, and geometric-spectral graph respectively to mine patterns in spectral, spatial, and geometric-spectral domains.Then, the multispectral points graph is generated by combining the spatial, spectral, and geometric-spectral graphs. Moreover,dimensionality features and spectrums are introduced to screen the appropriate connection points for constructing the spatial graph. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. A heat operator is then introduced to enhance the low-frequency filters and enforce the smoothness in the graph signal. Considering that different land covers have different scale characteristics, we use multiple scales instead of the single scale when leveraging heat operator on graph convolution. The experimental results on two real multispectral points data sets demonstrate the superiority of the proposed Ma SGCN to several state-of-the-art methods.

关 键 词：multispectral points multi-attribute graph construction smooth graph convolution graph convolutional network(GCN) 3D land cover classification 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] P237[自动化与计算机技术—控制科学与工程]