Iterative clustering based active learning for hyperspectral image classification

In this paper, a novel iterative clustering based active learning (ICAL) method for hyperspectral image classification is proposed. On the one hand, the extreme learning machine is combined with the Markov random field (ELM-MRF) for label assignment, to exploit both spectral and spatial information to boost classification result. On the other hand, an iterative clustering based sample selection strategy is introduced to optimally choose the most informative training sample set. This strategy first selects a candidate set of samples, according to the differential map that is obtained by comparing the ELM-MRF based classification results in adjacent iterations. Then, all the pixels in the candidate set are clustered according to spectral characteristics. Finally, from each cluster, the one sample with the highest uncertainty is added to the new training sample set. By this sample selection strategy, the diversity and uncertainty of training samples can be maximized, which can further contribute to the improvement of classification performance. Experimental results show that the proposed ICAL method can achieve competitive classification results even with a limited number of labeled samples.