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A novel spectral-spatial multi-scale network for hyperspectral image classification with the Res2Net block

Zhang, Zhongqiang ; Liu, Danhua ; Gao, Dahua ; Shi, Guangming

International journal of remote sensing, 2022-02, Vol.43 (3), p.751-777 [Periódico revisado por pares]

London: Taylor & Francis

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  • Título:
    A novel spectral-spatial multi-scale network for hyperspectral image classification with the Res2Net block
  • Autor: Zhang, Zhongqiang ; Liu, Danhua ; Gao, Dahua ; Shi, Guangming
  • Assuntos: Accuracy ; Artificial neural networks ; Classification ; Entropy ; Feature extraction ; hinge cross-entropy loss ; hyperspectral image classification ; Hyperspectral imaging ; Image classification ; Machine learning ; Methods ; Modules ; multi-scale spatial module ; multi-scale spectral module ; Neural networks ; Spectra ; the Res2Net block ; Training
  • É parte de: International journal of remote sensing, 2022-02, Vol.43 (3), p.751-777
  • Descrição: Traditional hyperspectral image (HSI) classification methods mainly include machine learning and convolutional neural network. However, they extremely depend on the large training samples. To obtain high accuracy on limited training samples, we propose a novel end-to-end spectral-spatial multi-scale network (SSMSN) for HSI classification. The SSMSN uses the multi-scale spectral module and the multi-scale spatial module to extract discriminative multi-scale spectral and multi-scale spatial features separately. In the multi-scale spectral module and spatial module, the multi-scale Res2Net block structure can learn multi-scale features at a granular level and increase the range of receptive fields by constructing the hierarchical residual-like connection within one single residual block. To alleviate the overfitting problem and further improve the classification accuracy on limited training samples, we adopt a simple but effective hinge cross-entropy loss function to train the SSMSN at the dynamic learning rate. A large number of experimental results demonstrate that on the Indiana Pines, University of Pavia, Kennedy Space Center, and Salinas Scene data sets, the proposed SSMSN achieves higher classification accuracy than state-of-the-art methods on limited training samples. Meanwhile, our SSMSN obtains less training and testing time than the popular AUSSC method.
  • Editor: London: Taylor & Francis
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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