LAUSR

While deep learning algorithms have achieved good results in hyperspectral image (HSI) classification, several supervised classification algorithms rely on a large amount of labeled samples to get adequate performance. Collecting a large amount of labeled samples is expensive in many real applications. To address this issue, a novel semi-supervised HSI classification framework called deep representative feature learning (DRFL) with virtual adversarial training (DRFL-VAT) is developed in this paper. 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 data sets and our results show that our method is competitive when compared to other state-of-the-art approaches.