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 p...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.展开更多
We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers ex...We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.展开更多
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)。
文摘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.
基金This work was supported by the National Key Research and Development Program of China under Grant 2018YFF0214704.
文摘We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.
