A Novel Semisupervised Active-Learning Algorithm for Hyperspectral Image Classification

Less training samples are a challenging problem in hyperspectral image classification. Active learning and semisupervised learning are two promising techniques to address the problem. Active learning solves the problem by improving the quality of the training samples, while semisupervised learning solves the problem by increasing the quantity of the training samples. However, they pay too much attention to the discriminative information in the unlabeled data, leading to information bias to train supervised models, and much more effort to label samples. Therefore, a method to discover representativeness and discriminativeness by semisupervised active learning is proposed. It takes advantages of both active learning and semisupervised learning. The representativeness and discriminativeness are discovered with a labeling process based on a supervised clustering technique and classification results. Specifically, the supervised clustering results can discover important structural information in the unlabeled data, and the classification results are also highly confidential in the active-learning process. With these clustering results and classification results, we can assign pseudolabels to the unlabeled data. Meanwhile, the unlabeled samples that cannot be assigned with pseudolabels with high confidence at each iteration are regarded as candidates in active learning. The methodology is validated on four hyperspectral data sets. Significant improvements in classification accuracy are achieved by the proposed method with respect to the state-of-the-art methods.