Spatial-Spectral Smooth Graph Convolutional Network for Multispectral Point Cloud Classification. Wang, Q., Zhang, X., & Gu, Y. International Geoscience and Remote Sensing Symposium (IGARSS), 2020.
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Paper doi abstract bibtex
Paper doi abstract bibtex Multispectral point cloud, as a new type of data 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 point cloud as a spatial-spectral graph and propose a smooth graph convolutional network for multispectral point cloud classification, abbreviated 3SGCN. We construct the spectral graph and spatial graph respectively to mine patterns in spectral and spatial geometric domains. Then, the multispectral point cloud graph is generated by combining the spatial and spectral graphs. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. Heat operator is introduced to enhance the low- frequency filters and enforce the smoothness in the graph signal. Further, a graph -based smoothness prior is deployed in our loss function. Experiments are conducted on real multispectral point cloud. The experimental results demonstrate that 3 SGCN can achieve significant improvements in comparison with several state-of-the art algori thms.
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title = {Spatial-Spectral Smooth Graph Convolutional Network for Multispectral Point Cloud Classification},
type = {article},
year = {2020},
keywords = {3D land cover classification,Multispectral point cloud,graph convolutional network,graph smoothness,spatial-spectral graph construction},
pages = {1062-1065},
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abstract = {Multispectral point cloud, as a new type of data 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 point cloud as a spatial-spectral graph and propose a smooth graph convolutional network for multispectral point cloud classification, abbreviated 3SGCN. We construct the spectral graph and spatial graph respectively to mine patterns in spectral and spatial geometric domains. Then, the multispectral point cloud graph is generated by combining the spatial and spectral graphs. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. Heat operator is introduced to enhance the low- frequency filters and enforce the smoothness in the graph signal. Further, a graph -based smoothness prior is deployed in our loss function. Experiments are conducted on real multispectral point cloud. The experimental results demonstrate that 3 SGCN can achieve significant improvements in comparison with several state-of-the art algori thms.},
bibtype = {article},
author = {Wang, Qingwang and Zhang, Xiangrong and Gu, Yanfeng},
doi = {10.1109/IGARSS39084.2020.9324584},
journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}
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