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A local proximity based data regularization framework for active learning is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. Based on the "Consistency Assumption", a local k-nearest neighborhood Laplacian Graph based regularizer is constructed to explore local inconsistency that often results from insufficient description of the current learner for the data space. Two graph regularization methods, which differ in the approach used to construct the graph weights, are investigated. One utilizes only spectral information, while the other further incorporates local spatial information through a composite Gaussian heat kernel. The regularizer queries samples with greatest violation of the smoothness assumption based on the current model, then adjusts the decision function towards the direction that is most consistent with both labeled and unlabeled data. Experiments show excellent performance on both unlabeled and unseen data for 10 class hyperspectral image data acquired by AVIRIS, as compared to random sampling and the state-of-the-art SVMSIMPLE.