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Proceedings Paper

Lessons learned: technology transfer from terrestrial spectroscopy to biomedicine
Author(s): Glenn Healey; David Slater
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Paper Abstract

The spectral radiance measured by an imaging spectrometer for a material on the earth's surface has significant dependence on environmental factors such as the illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely on hyperspectral image data without associated ground truth information. An important advantage of hyperspectral data is that the sensor spectral dimensionality typically exceeds the dimensionality of the signature variability for any material of interest. We have shown, for example, that the set of observed 0.4 - 2.5 micrometers spectral radiance vectors for a material on the earth's surface lies in a low-dimensional subspace of the hyperspectral measurement space. This analysis has led to robust algorithms for invariant subpixel image analysis that have been applied to a number of remote sensing applications. Similar computational methods can be applied to biomedical images by introducing variability models for the signatures of interest. We present results for material identification in remote sensing images as well as for the quantification of cell population in 3D brain tissue samples.

Paper Details

Date Published: 14 March 2000
PDF: 12 pages
Proc. SPIE 3920, Spectral Imaging: Instrumentation, Applications, and Analysis, (14 March 2000); doi: 10.1117/12.379583
Show Author Affiliations
Glenn Healey, Univ. of California/Irvine (United States)
David Slater, Univ. of California/Irvine (United States)


Published in SPIE Proceedings Vol. 3920:
Spectral Imaging: Instrumentation, Applications, and Analysis
Gregory H. Bearman; Dario Cabib; Richard M. Levenson, Editor(s)

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