Active learning of hyperspectral data with spatially dependent label acquisition costs

Supervised learners can be used to automatically classify many types of spatially distributed data. For example, land cover classification by hyperspectral image data analysis is an important remote sensing task where a supervised learner is trained on a large set of labeled data. However, while gathering unlabeled samples may be relatively easy, labeling large amounts of data can be very costly. Acting learning is one approach to reduce the amount of labeled data required to build a supervised learner that performs well. However, most active learning approaches assume that the cost of acquiring labels for all points is uniform. For spatially distributed data that requires physical access to spatial locations in order to assign labels, label acquisition costs become proportional to distance traveled in order to label a point. In this paper, we present results for applying a novel active learning method which takes variable label acquisition costs into account on two hyperspectral datasets.