Computational Efficiency Active Learning For Classification Of Hyperspectral Images

Active learning usually is conducted in an iterative way. In the paper, a Computational Efficiency Active Learning (CEAL) algorithm is proposed to address this problem based on diversity measurement for classification of hyperspectral images. In particular, each unlabeled sample is pre-assigned a group label, which can be carried out by such as a clustering algorithm. After that, candidate patterns are selected from each group to satisfy the diversity assumption in each round. The proposed CEAL algorithm is validated by real hyperspectral images. Experimental results show that the proposed CEAL algorithm can obtain not only high classification accuracies but also yield a two to four order of magnitude increase in computational efficiency.