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Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification

Remote sensing (Basel, Switzerland), 2024-03, Vol.16 (5), p.895 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs16050895

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  • Title:
    Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification
  • Author: Liu, Shuai ; Li, Hongfei ; Jiang, Chengji ; Feng, Jie
  • Subjects: Artificial neural networks ; auxiliary classifier ; bidirectional LSTM ; Classification ; Classifiers ; Complexity ; Datasets ; Discriminant analysis ; graph convolutional networks ; Graphs ; hyperspectral image classification ; Hyperspectral imaging ; Image classification ; Labels ; Methods ; Modules ; Neural networks ; spectral–spatial information ; Training
  • Is Part Of: Remote sensing (Basel, Switzerland), 2024-03, Vol.16 (5), p.895
  • Description: The classifiers based on the convolutional neural network (CNN) and graph convolutional network (GCN) have demonstrated their effectiveness in hyperspectral image (HSI) classification. However, their performance is limited by the high time complexity of CNN, spatial complexity of GCN, and insufficient labeled samples. To ease these limitations, the spectral–spatial graph convolutional network with dynamic-synchronized multiscale features is proposed for few-shot HSI classification. Firstly, multiscale patches are generated to enrich training samples in the feature space. A weighted spectral optimization module is explored to evaluate the discriminate information among different bands of patches. Then, the adaptive dynamic graph convolutional module is proposed to extract local and long-range spatial–spectral features of patches at each scale. Considering that features of different scales can be regarded as sequential data due to intrinsic correlations, the bidirectional LSTM is adopted to synchronously extract the spectral–spatial characteristics from all scales. Finally, auxiliary classifiers are utilized to predict labels of samples at each scale and enhance the training stability. Label smoothing is introduced into the classification loss to reduce the influence of misclassified samples and imbalance of classes. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art methods, obtaining overall accuracies of 87.25%, 92.72%, and 93.36% on the Indian Pines, Pavia University, and Salinas datasets, respectively.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs16050895
  • Source: Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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