Generic visual analysis for multi- and hyperspectral image data

  • Authors:

  • Björn Labitzke;Serkan Bayraktar;Andreas Kolb

  • Affiliations:

  • Computer Graphics Group, Institute for Vision and Graphics, Faculty IV: Science and Technology, University of Siegen, Siegen, Germany;Computer Graphics Group, Institute for Vision and Graphics, Faculty IV: Science and Technology, University of Siegen, Siegen, Germany;Computer Graphics Group, Institute for Vision and Graphics, Faculty IV: Science and Technology, University of Siegen, Siegen, Germany

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2013

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Abstract

Multi- and hyperspectral imaging and data analysis has been investigated in the last decades in the context of various fields of application like remote sensing or microscopic spectroscopy. However, recent developments in sensor technology and a growing number of application areas require a more generic view on data analysis, that clearly expands the current, domain-specific approaches. In this context, we address the problem of interactive exploration of multi- and hyperspectral data, consisting of (semi-)automatic data analysis and scientific visualization in a comprehensive fashion. In this paper, we propose an approach that enables a generic interactive exploration and easy segmentation of multi- and hyperspectral data, based on characterizing spectra of an individual dataset, the so-called endmembers. Using the concepts of existing endmember extraction algorithms, we derive a visual analysis system, where the characteristic spectra initially identified serve as input to interactively tailor a problem-specific visual analysis by means of visual exploration. An optional outlier detection improves the robustness of the endmember detection and analysis. An adequate system feedback of the costly unmixing procedure for the spectral data with respect to the current set of endmembers is ensured by a novel technique for progressive unmixing and view update which is applied at user modification. The progressive unmixing is based on an efficient prediction scheme applied to previous unmixing results. We present a detailed evaluation of our system in terms of confocal Raman microscopy, common multispectral imaging and remote sensing.