Multi-attribute smooth graph convolutional network for multispectral points classification

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 multi-attribute smooth graph convolutional network (MaSGCN) 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 MaSGCN to several state-of-the-art methods.