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 ...
People also ask
Can GNN be used for image classification?
What is PCA for hyperspectral image classification?
What is hyperspectral image classification?
What is band selection in hyperspectral image classification?
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.