Spatially Adapted Manifold Learning for Classification of Hyperspectral Imagery with Insufficient Labeled Data

A classifier derived from labeled samples acquired over an extended area may not perform well for a specific sub-region if the spectral signatures of classes vary across the image. However, characterizing the local effects are an ill-posed problem, particularly for hyperspectral data, since an adequate number of labeled samples is not typically available for every location. This problem is addressed using semi-supervised learning and manifold learning, which both exploit the information provided by unlabeled samples in the image. A spatially adaptive classification method that uses Laplacian regularization is proposed, with the updating scheme using a combination of labeled and unlabeled samples.