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Spectral and spatial regularized semi-supervised learning is investigated for classification of hyperspectral images. In spectral regularizer, sum of minimum distance (SMD) is introduced to determine graph adjacency relationships and local manifold learning (LML) is employed for edge weighting. The resulted SMD_LML regularizer is able to constrain the prediction vectors to preserve the local geometry of each neighborhood. In spatial regularizer, spatial neighbors with similar spectra are enforced to have similar predictions. The two regularizers capture …