A NEW SEMI-SUPERVISED APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION WITH DIFFERENT ACTIVE LEARNING STRATEGIES

Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semi-supervised learning (SSL) techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new framework for SSL which exploits active learning (AL) for unlabeled sample selection. Specifically, we use AL to select the most informative unlabeled training samples and further evaluate two different strategies for active sample selection. In this work, the proposed approach is illustrated with the sparse multinomial logistic regression (SMLR) classifier learned with the MLR via variable splitting and augmented Lagrangian (LORSAL) algorithm. Our experimental results with a real hyperspectral image collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the use of AL for unlabeled sample selection represents an effective and promising strategy in the context of semi-supervised hyperspectral data classification.