Improved hybrid convolutional neural network combined with attention mechanism for hyperspectral image classification

Cailing Wang; Yiming Wang; Jing Zhang

doi:10.1117/12.2681372

8 June 2023 Improved hybrid convolutional neural network combined with attention mechanism for hyperspectral image classification

Cailing Wang, Yiming Wang, Jing Zhang

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Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270713 (2023) https://doi.org/10.1117/12.2681372
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

To address the problems of high dimensionality of hyperspectral images, small training samples and overfitting and too many parameters caused by model training, a hyperspectral image classification model (CBAM-HybridSN) with improved hybrid neural network combined with convolutional attention mechanism is proposed. The model firstly uses principal component analysis to remove the redundancy of spectral dimensional data, extracts the null spectral features by the hybrid neural network model, and introduces the convolutional attention module to rescale the extracted features and highlight the important features, thus improving the classification accuracy. In the experiments, the Pavia University dataset was divided into samples with 1:9, and the OA reached 99.32%, achieving accurate classification of hyperspectral images with small samples.

Citation Download Citation

Cailing Wang, Yiming Wang, and Jing Zhang "Improved hybrid convolutional neural network combined with attention mechanism for hyperspectral image classification", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270713 (8 June 2023); https://doi.org/10.1117/12.2681372

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KEYWORDS

Hyperspectral imaging

Image classification

Feature extraction

Education and training

Convolutional neural networks

Convolution

Data modeling

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