Semi-supervised active learning for urban hyperspectral image classification

In this paper, we develop a new framework for semi-supervised learning which exploits active learning for unlabeled sample selection in hyperspectral data classification. Specifically, we use active learning to select the most informative unlabeled training samples with the ultimate goal of systematically achieving noticeable improvements in classification results with regard to those found by randomly selected training sets of the same size. Our experimental results, conducted with an urban hyperspectral scene collected by the Reflective Optics Spectrographic Imaging Instrument (ROSIS) of the Deutschen Zentrum for Luftund Raumfahrt (DLR, the German Aerospace Agency) over the city of Pavia, Italy, indicate that using active learning for unlabeled sample selection represents an effective and promising strategy in the context of urban hyperspectral data classification.