Paper Info
Title
DRFL-VAT: Deep Representative Feature Learning With Virtual Adversarial Training for Semisupervised Classification of Hyperspectral Image
Abstract
While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large number of labeled samples to get adequate performance. Collecting a large number of labeled samples is expensive in many real applications. To address this issue, a novel semisupervised HSI classification framework called deep representative feature learning (DRFL) with virtual adversarial training (DRFL-VAT) is developed in this article. By embedding the local manifold learning (LML) into the fully connected layers of a convolutional neural network (CNN), our newly developed DRFL can learn representative features. The VAT regularization is adopted to exploit the prediction label distribution of training samples and addresses the overfitting problem. Finally, the objective function of DRFL-VAT is solved by a customized algorithm. We test our method on three widely public HSI datasets and our results show that our method is competitive when compared to other state-of-the-art approaches.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3187187
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Feature extraction, Representation learning, Training, Task analysis, Convolutional neural networks, Linear programming, Deep learning, Convolutional neural network (CNN), hyperspectral image (HSI) classification, local manifold learning (LML), semisupervised learning, virtual adversarial training (VAT)
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jialong Chen100.34
Yuebin Wang201.01
Liqiang Zhang324136.21
Meiling Liu400.34
Antonio Plaza53475262.63