Active and Semisupervised Learning With Morphological Component Analysis for Hyperspectral Image Classification

Classification of hyperspectral images has recently gained significant popularity due to both the development of remote sensing technologies and the advances in image analysis approaches. One crucial step to achieve accurate classification is to acquire sufficient high-quality training data, which is often a time-consuming and expensive process. To alleviate this burden, in this letter, we propose an active and semisupervised learning (SSL) approach that utilizes morphological component analysis (MCA) for classification of hyperspectral images. First, the original hyperspectral data are decomposed into its morphological components via MCA. In each feature domain, the active learning (AL) and SSL are combined to enlarge the training data set based on superpixels. Finally, decision fusion is carried out to integrate the predictions from the two components. The proposed method is tested on both benchmark and real world application hyperspectral data sets. Experimental results indicate that the proposed method can lead to a better classification with respect to the conventional AL approaches.