A novel adaptive classification method for hyperspectral data using manifold regularization kernel machines

Remote sensing data sets are often difficult to compare directly due to environmental changes between acquisitions of two data sets. This paper proposes an adaptive framework for robust classification when no reference data are available in a new area or time period. Labels of test data are recovered during iterative applications of kernel machines by reflecting geometry of unlabeled samples via the manifold regularization term, so that the labeled/unlabeled samples form clusters on the data manifold. A one-against-one scheme is used for the extension of the binary classifier to multiclass problems, where semi-labels are used for iterative training of classifier. The proposed method is applied to a series of data pair of Hyperion and AVIRIS hyperspectral data and compared to other non-adaptive classification methods.