Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery

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Abstract

Schroedinger Eigenmaps (SE) has recently emerged as a powerful graph-based technique for semi-supervised manifold learning and recovery. By extending the Laplacian of a graph constructed from hyperspectral imagery to incorporate barrier or cluster potentials, SE enables machine learning techniques that employ expert/labeled information provided at a subset of pixels. In this paper, we show how different types of nondiagonal potentials can be used within the SE framework in a way that allows for the integration of spatial and spectral information in unsupervised manifold learning and recovery. The nondiagonal potentials encode spatial proximity, which when combined with the spectral proximity information in the original graph, yields a framework that is competitive with state-of-the-art spectral/spatial fusion approaches for clustering and subsequent classification of hyperspectral image data.

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