Active learning for hyperspectral image classification using sparse code histogram and graph-based spatial refinement. Issue 3 (1st February 2017) - Ni, Ding; Ma, Hongbing
ABSTRACT: In order to address the challenge of hyperspectral image (HSI) classification with very limited labelled samples, active learning (AL) has become a hot research issue in recent years. Although lots of AL approaches have been proposed in the literature, most of them concentrate on how to select the most informative samples, while ignore the significance of the input feature. We believe that the input feature and the query selection are both crucial for constituting an efficient AL algorithm. In this article, we propose a new discriminative feature, sparse code histogram (SCH), to conduct the AL procedure. SCH exhibits a much stronger distinguishability than several other widely used features, and thus a better AL performance could be expected. With this novel input feature, a probabilistic classifier, multinomial logistic regression, is trained to obtain the class probability of each sample. Considering that the class probability is usually biased due to the limited labelled samples, a graph-based spatial refinement is proposed to refine the class probability by exploiting the contextual information. Based on the refined class probability, informative samples are selected for manual labelling and classifier retraining. Such a process is iterated until a stopping criterion is met. Experimental results demonstrate that the proposed method could usually achieve above 90% classification accuracy with only two iterations, which significantly outperforms several state-of-the-art approaches.