Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies

  • Sofya ChepushtanovaEmail author
  • Michael Kirby
  • Chris Peterson
  • Lori Ziegelmeier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9667)


The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets. In this paper, we apply persistent homology to the characterization of gas plumes in time dependent sequences of hyperspectral cubes, i.e. the analysis of 4-way arrays. We investigate hyperspectral movies of Long-Wavelength Infrared data monitoring an experimental release of chemical simulant into the air. Our approach models regions of interest within the hyperspectral data cubes as points on the real Grassmann manifold G(kn) (whose points parameterize the k-dimensional subspaces of \(\mathbb {R}^n\)), contrasting our approach with the more standard framework in Euclidean space. An advantage of this approach is that it allows a sequence of time slices in a hyperspectral movie to be collapsed to a sequence of points in such a way that some of the key structure within and between the slices is encoded by the points on the Grassmann manifold. This motivates the search for topological features, associated with the evolution of the frames of a hyperspectral movie, within the corresponding points on the Grassmann manifold. The proposed mathematical model affords the processing of large data sets while retaining valuable discriminatory information. In this paper, we discuss how embedding our data in the Grassmann manifold, together with topological data analysis, captures dynamical events that occur as the chemical plume is released and evolves.


Grassmann manifold Persistent homology Hyperspectral imagery Signal detection Topological data analysis 



This paper is based on research partially supported by the National Science Foundation grants DMS-1228308, DMS-1322508. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sofya Chepushtanova
    • 1
    Email author
  • Michael Kirby
    • 2
  • Chris Peterson
    • 2
  • Lori Ziegelmeier
    • 3
  1. 1.Wilkes UniversityWilkes-BarreUSA
  2. 2.Colorado State UniversityFort CollinsUSA
  3. 3.Macalester CollegeSaint PaulUSA

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