Locality-preserving nonnegative matrix factorization for hyperspectral image classification

Feature extraction based on nonnegative matrix factorization is considered for hyperspectral image classification. One shortcoming of most remote-sensing data is low spatial resolution, which causes a pixel to be mixed with several pure spectral signatures, or endmembers. To counter this effect, locality-preserving nonnegative matrix factorization is employed in order to extract an endmembers-based feature representation as well as to preserve the intrinsic geometric structure of hyperspectral data. Subsequently, a Gaussian mixture model classifier is employed in the induced-feature subspace. Experimental results demonstrate that the proposed classification system significantly outperforms traditional approaches even in instances of limited training data and severe pixel mixing.