| Autor: |
Liu, Bing, Zuo, Xibing, Yu, Anzhu, Sun, Yifan, Wang, Ruirui |
| Předmět: |
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| Zdroj: |
Remote Sensing Letters; May2023, Vol. 14 Issue 5, p479-490, 12p |
| Abstrakt: |
Recently, deep-learning methods have significantly promoted the development of hyperspectral image classification. However, these methods are still limited by the lack of labelled samples. This study proposes a semi-supervised deep-learning method based on multi-view consistency to improve the classification accuracy of hyperspectral images using a few labelled samples. First, a classifier was built based on a residual network. Subsequently, an attention mechanism was introduced to enhance the classification performance of the residual network. Naturally, the class corresponding to different feature representations of the same sample should be consistent; this was used as an unsupervised constraint to assist model training. Therefore, the consistency of the model prediction of different views from the same unlabelled samples was calculated as the unsupervised loss. Finally, the sum of supervised and unsupervised losses was used as the final loss function to train the deep-learning model. The introduction of the unsupervised loss function enabled our model to fully use unlabelled samples to improve classification accuracy. Extensive classification experiments performed on two real hyperspectral image datasets demonstrated that the proposed method achieved higher classification accuracy than the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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