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.
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Multi-scale attention-based few-shot hyperspectral images classification
Semantic Scholar · Environmental Science · 2023
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.