Abstract:
Multispectral point clouds provide the data basis for finer land cover classification due to the simultaneous spatial and spectral information. How to jointly utilize spa...Show MoreMetadata
Abstract:
Multispectral point clouds provide the data basis for finer land cover classification due to the simultaneous spatial and spectral information. How to jointly utilize spatial-spectral information becomes a hot research direction. Benefiting from the excellent performance of graph neural networks (GNNs) on non-Euclidean data, it is well suited to modelling multispectral point clouds to achieve higher classification accuracy. This paper proposes a novel graph convolutional networks with multi-kernel learning (GCN-MKL) for adaptively constructing a graph of multispectral point cloud for finer classification. Specifically, we use multiple base kernels to map the multispectral point cloud into a high-dimensional feature space and learn a linear combination of base kernels through a multi-kernel learning mechanism embedded in the network. The learned multi-kernel graph can effectively measure the high-dimensional similarity between multispectral points. Experimental results demonstrate that the proposed GCN-MKL outperforms several state-of-the-art methods on a real multispectral point cloud.
Date of Conference: 16-21 July 2023
Date Added to IEEE Xplore: 20 October 2023
ISBN Information: