An Active Learning Approach to Knowledge Transfer for Hyperspectral Data Analysis

Obtaining ground truth for classification of remotely sensed data is time consuming and expensive. In addition, a number of factors cause the spectral signatures of the same class to vary spatially. Therefore, successful adaptation of a classifier designed from available labeled data to classify new images acquired over other geographic locations is difficult but invaluable to the remote sensing community. In this paper we propose an active learning technique for rapidly updating existing classifiers using very few labeled data points from the new image. We also show empirically that our updated classifier exhibits better learning rates than classifiers trained via other active learning and semi-supervised methods.