Spectral–Spatial Residual Graph Attention Network for Hyperspectral Image Classification

Xu, Kejie; Zhao, Yue; Zhang, Lingming; Gao, Chenqiang; Hong, Huixiao

doi:10.1109/lgrs.2021.3111985

Cited by 21 publications

(7 citation statements)

References 35 publications

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“…GAT [36][37][38] is a variant of Graph Neural Network (GNN) and mainly consists of a graph attention layer (GAL). GAT iteratively updates the representation of each node by aggregating the representations of neighboring nodes using a multi-head attention network mechanism, thus enabling the adaptive assignment of weights to different neighboring nodes.…”

Section: Graph Attention Networkmentioning

confidence: 99%

“…Sha et al [37] applied a graph attention mechanism network to the HSIC and represented the relationship between neighboring nodes adaptively, but did not fully use the deep spatial-spectral features of the images when constructing the graph structure. An HSIC model (S2RGANet) using a spatial-spectral residual graph attention mechanism was proposed by Xu et al [38]. This model effectively improves the classification accuracy by constructing a deep convolutional residual module while introducing a network of graph attention mechanisms to obtain more important spatial information, but the training time of this network model is long.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Zhang

Huanhuan

et al. 2023

Preprint

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Convolutional neural networks and graph convolutional neural networks are two classical deep learning models that have been widely used in hyperspectral image classification tasks with remarkable achievements. However, hyperspectral image classification models based on graph convolutional neural networks using only shallow spectral or spatial features are insufficient to provide reliable similarity measures for constructing graph structures, limiting their classification performance. To address this problem, we propose a hyperspectral image classification model combining 3D-2D hybrid convolution and a graph attention mechanism. First, a 3D-2D hybrid convolutional network is constructed and used to rapidly extract deep features that express spatial and spectral associations. Then, the graph is built based on deep spatial-spectral features to enhance the feature representation of the graph. Finally, a network of graph attention mechanisms is adopted to learn long-range spatial connections and to classify them using the extracted spatial features. The experimental results on two datasets, Indian Pine and the University of Pavia, show that the proposed method can achieve higher classification accuracy compared with other advanced methods.

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Section: Graph Attention Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Zhang

Huanhuan

et al. 2023

Preprint

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“…Model performance inevitably meets the bottleneck in complex scenes that need to be finely classified, Hong provides a general multimodal deep learning (MDL) framework which applies to pixel-wise classification tasks and spatial information models [54]. Xu [55] find that the conventional convolution kernels can not process rich spatial information effectively, so the spectral-spatial residual graph attention network is proposed including spectral residual and graph attention convolution modules. Hong proposed SpectralFormer network to enhance the representation of the sequence attributes of spectral signatures, which learn spectrally local sequence information from neighboring bands [56].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on 3-D Multihead Self-Attention Spectral–Spatial Feature Fusion Network

Qigao

Zhou

Shen

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural networks are a popular method in hyperspectral image classification. However, the accuracy of the models is closely related to the number and spatial size of training samples. Which relieve the performance decline by the number and spatial size of training samples, we designed a 3D multi-head self-attention spectral-spatial feature fusion network (3DMHSA-SSFFN) that contains step-by-step feature extracted blocks (SBSFE) and 3D multi-head-self-attention-module (3DMHSA). The proposed step-by-step feature extracted blocks relieved the declining-accuracy phenomenon for the limited number of training samples. Multi-scale convolution kernels extract more spatial-spectral features in the step-by-step feature-extracted blocks. In hyperspectral image classification, the 3DMHSA module enhances the stability of classification by correlating disparate features. Experimental results show that 3DMHSA-SSFFN possesses a better classification performance than other advanced models through the limited number of balance and imbalance training data in three data.

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“…This has stimulated interest among researchers in the community of HSI classification. Representative DL methods include convolutional neural networks (CNNs) [19], recurrent neural networks (RNNs) [20], [21], graph convolutional networks (GCNs) [22], [23], and capsule networks (CapsNets) [24], among which CNNs have been widely applied to HSI classification. Hu et al [25] and Chen et al [26] first employed CNNs for HSI classification in the spectral, spatial, and spatial-spectral domains.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fully Connected Tensorized Compression Network for Hyperspectral Image Classification

Lin

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Deep learning models, such as convolutional neural networks (CNNs), have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this article, we propose a new compression network [namely, a Hybrid Fully Connected Tensorized Compression Network (HybridFCTCN)] by considering the high dimensionality of HSI data. First, using the low-rank fully connected tensor network decomposition (FCTND), three novel units, i.e., FCTN-FC, FCTNConv2D, and FCTNConv3D, are designed to compress the weight tensor of standard fully connected (FC) layer and kernel tensor of convolutional layer, reducing their parameters. In the novel units, the intrinsic correlation of the decomposed factors is adequately exploited by the FC structures, which enhances their feature extraction and classification abilities. Then, benefiting from the hybrid network backbone composed of the FCTNConv3D and FCTNConv2D units, HybridFCTCN can extract more discriminative features with fewer parameters, while it has great generalization capability and robustness, enabling better HSI classification. Finally, the rank of above-designed units is defined, and its determination is discussed to facilitate the application of the proposed model. Extensive experiments on three widely used HSI datasets reveal that the proposed model achieves state-of-the-art classification performance for different training sample sizes with a very small number of parameters.

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Spectral–Spatial Residual Graph Attention Network for Hyperspectral Image Classification

Cited by 21 publications

References 35 publications

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

Hyperspectral Image Classification Based on 3-D Multihead Self-Attention Spectral–Spatial Feature Fusion Network

Hybrid Fully Connected Tensorized Compression Network for Hyperspectral Image Classification

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