An Efficient Active Learning Algorithm with Knowledge Transfer for Hyperspectral Data Analysis

We propose an active learning algorithm with knowledge transfer for classification of hyperspectral remote sensing data. The proposed method is based on a previously proposed algorithm, but yields faster learning curves by adjusting distributions of labeled data differently for the old and the new data. With the proposed method, the classifier can effectively transfer its knowledge learned from one region to a spatially or temporally separated region whose spectral signature is different. Empirical evaluation of the proposed algorithm is performed for two different hyperspectal datasets.