Gabor-based active learning for hyperspectral image classification

Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data are often highly mixed, i.e., most samples fluctuate in a local density areas. In this case, the potential of active learning for effective training sample selection is more limited. In order to address this relevant issue, we develop a new Gabor-based active learning approach for hyperspectral image classification, which consists of two main steps. First, we use a Gabor filter for feature extraction, which aims at bringing the data into a discriminative space. Then, we perform active learning to find the most informative training samples in the low density areas prior to the final classification. Our experimental results, conducted using two real hyperspectral datasets, indicate that the proposed Gabor-based approach can greatly improve the potential of active learning for classification purposes.