Mar 15, 2023 · A novel two-branch deeper GCN (TBDGCN) is proposed to combine the advantages of superpixel-based GCN and pixel-based CNN, which can simultaneously extract ...
In the CNN branch, to capture spatial positional information and channel information, a mixed attention mechanism is constructed to extract attention-based ...
Graph convolutional neural networks (GCNs) have been widely used in Hyperspectral Images (HSIs) classification, which have advantage in processing Non-Euclidean ...
A dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel ...
Jul 12, 2024 · Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC).
This paper presents a comprehensive review of GCN-based hyperspectral image classification methods. The review covers five aspects.
Missing: Deeper | Show results with:Deeper
This paper proposes a novel dual-branch difference amplification GCN (D2AGCN) for HSI change detection with limited samples, which allows the network to ...
This toolbox consists of eight hyperspectral classification networks as follows 1DCNN: one-dimensional convolutional neural network 2DCNN: two-dimensional ...
Missing: Deeper | Show results with:Deeper
This paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model ( ...
Aug 6, 2020 · In this paper, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification.
Missing: Two- Branch Deeper