Combining Semi-Supervised and Active Learning for Hyperspectral Image Classification

Hyperspectral image classification is difficult due to the high dimensional features, high intraclass variance, low interclass variance but limited training samples. In this paper, the ECASSL (Ensured Collaborative Active and Semi-Supervised Labeling) approach, which attempts to exploit pseudo-labeled samples to improve the performance of active learning based hyperspectral image classification, is proposed. In detail, in each round of active query, we obtain new human labeled samples from active query strategy and pseudo-labeled samples from the current trained classifier collaboratively. After that, we update the classifier base on latest labeled and pseudo-labeled samples. And then we correct those pseudo-labels obtained from previous iterations with the new classifier. Finally, we train the final classifier base on both the labeled samples and pseudo-labeled samples. The experiment results show that our algorithm significantly reduced the need of labeled samples while achieving comparable performance when compared with state-of-the-art algorithms for hyperspectral image classification.