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

Nathan D. Cahill; Wojciech Czaja; David W. Messinger

doi:10.1117/12.2050651

13 June 2014 Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery

Nathan D. Cahill, Wojciech Czaja, David W. Messinger

Author Affiliations +

Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX; 908804 (2014) https://doi.org/10.1117/12.2050651
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Nathan D. Cahill, Wojciech Czaja, and David W. Messinger "Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908804 (13 June 2014); https://doi.org/10.1117/12.2050651

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