Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines

Localized training data typically utilized to develop a classifier may not be fully representative of class signatures over large areas but could potentially provide useful information which can be updated to reflect local conditions in other areas. An adaptive classification framework is proposed for this purpose, whereby a kernel machine is first trained with labeled data and then iteratively adapted to new data using manifold regularization. Assuming that no class labels are available for the data for which spectral drift may have occurred, resemblance associated with the clustering condition on the data manifold is used to bridge the change in spectra between the two data sets. Experiments are conducted using spatially disjoint data in EO-1 Hyperion images, and the results of the proposed framework are compared to semisupervised kernel machines.