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A Prototype and Active Learning Network for Small-Sample Hyperspectral Image Classification
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DOI：10.1109/LGRS.2023.3324398.png)
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Abstract
En 中文
In recent years, with the continuous development of deep learning (DL), neural networks have demonstrated good results in large-sample hyperspectral image (HSI) classification. However, in practice, labels are often limited. In order to use fewer labeled samples without degrading the classification performance, this letter proposes a new semi-supervised classification method named prototype and active learning network (PALN), which integrates DL, active learning (AL), and prototype learning (PL) into a framework. After training the DL network with a small number of available labels, samples with high uncertainty are selected by AL to assign true labels, while samples more similar with prototypes are chosen by PL with their pseudo labels, and all selected samples are appended to the training set for the next training. Compared with existing classification methods, our method achieves good performance on two hyperspectral datasets.
Keywords:
Prototypes
Training
Hyperspectral imaging
Uncertainty
Principal component analysis
Convolutional neural networks
Sun
Active learning (AL)
deep learning (DL)
hyperspectral image (HSI) classification
prototype learning (PL)
Journal
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16.4
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