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We propose a Multi-level Graph Learning Network (MGLN) for HSI classification, where the graph structural information at both local and global levels can be ...
Hence, we propose a Multi-level Graph Learning Network (MGLN) for HSI classification, where the graph structural information at both local and global levels can ...
Sep 19, 2020 · In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph ...
Oct 22, 2024 · More significantly, our miniGCN is capable of inferring out-of-sample data without re-training networks and improving classification performance ...
Apr 8, 2024 · The overall accuracies are 97.75%, 99.96%, 92.28%, and 95.73%, which significantly outperforms the current state-of-the-art clustering methods.
PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image Classification Using Multiple Spatial-Spectral Features. by. Ningbo Guo.
Multiscale Dynamic Graph Convolutional Network for hyperspectral image classification ... Multi-Level Graph Learning Network for Hyperspectral Image ...
Abstract—Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image clas- sification due to their ability to ...
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Apr 10, 2023 · In this article, to make good use of the advantages of CNN and GCN, we propose a novel multiple feature fusion model termed attention multihop ...
In this article, a novel multi-scale feature learning via residual dynamic graph convolutional network is designed for HSI classification.