An MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery

Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.