Multi-scale attention-based few-shot hyperspectral images classification

Lanwei Ding; Guo Cao; Ling Xu; Lindiao Deng; Hao Xu; Qikun Pan; Yanfeng Shang

doi:10.1117/12.2680022

27 June 2023 Multi-scale attention-based few-shot hyperspectral images classification

Lanwei Ding, Guo Cao, Ling Xu, Lindiao Deng, Hao Xu, Qikun Pan, Yanfeng Shang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051D (2023) https://doi.org/10.1117/12.2680022
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In recent years, deep learning-based hyperspectral image classification techniques have developed rapidly. Many effective deep learning models have been proposed in academia, such as 3D-CNN and some other CNN-based methods, which have achieved high accuracy in hyperspectral image classification. These excellent methods rely on large number of labeled samples for their effectiveness. In practice, labeling pixels of hyperspectral images is expensive (time-consuming and labor-intensive), so it is often difficult to obtain enough labeled samples for training deep neural network models. To address this problem, we propose a multiscale attention-based few-shot learning (MAFSL) method using only a few labeled samples for each category in this paper. First, few-shot learning is performed on mini-ImageNet to obtain prior knowledge, and then the knowledge is transferred to the hyperspectral dataset. Before embedding features, multiscale attention-based feature extraction with reconstruction loss is applied to the hyperspectral image. Then, the obtained features are input into the spatial feature extraction network and the spectral extraction network, respectively. Finally, the embedded features are put into the metric space for classification. The proposed model can get a higher classification accuracy because the extracted features have less correlation with each other. Experimental results show that our MAFSL outperforms many existing supervised learning methods when only a small number of labeled samples are used.

Citation Download Citation

Lanwei Ding, Guo Cao, Ling Xu, Lindiao Deng, Hao Xu, Qikun Pan, and Yanfeng Shang "Multi-scale attention-based few-shot hyperspectral images classification", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051D (27 June 2023); https://doi.org/10.1117/12.2680022

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KEYWORDS

Feature extraction

Machine learning

Education and training

Convolution

Hyperspectral imaging

Image classification

Neural networks

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