Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification

Gaussian process (GP) classifiers represent a powerful and interesting theoretical framework for the Bayesian classification of hyperspectral images. However, the collection of labeled samples is time consuming and costly for hyperspectral data, and the training samples available are often not enough for an adequate learning of the GP classifier. Moreover, the computational cost of performing inference using GP classifiers scales cubically with the size of the training set. To address the limitations of GP classifiers for hyperspectral image classification, reducing the label cost and keeping the training set in a moderate size, this paper introduces an active learning (AL) strategy to collect the most informative training samples for manual labeling.

First, we propose three new AL heuristics based on the probabilistic output of GP classifiers aimed at actively selecting the most uncertain and confusing candidate samples from the unlabeled data. Moreover, we develop an incremental model updating scheme to avoid the repeated training of the GP classifiers during the AL process. The proposed approaches are tested on the classification of two realworld hyperspectral data. Comparison with random sampling method reveals a better accuracy gain and faster convergence with the number of queries, and comparison with recent active learning approaches shows a competitive performance. Experimental results also verified the efficiency of the incremental model updating scheme.