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Proceedings Paper

Constrained energy minimization applied to apparent reflectance and single-scattering albedo spectra: a comparison
Author(s): Ronald G. Resmini; William R. Graver; Mary E. Kappus; Mark E. Anderson
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Paper Abstract

Constrained energy minimization (CEM) has been applied to the mapping of the quantitative areal distribution of the mineral alunite in an approximately 1.8 km2 area of the Cuprite mining district, Nevada. CEM is a powerful technique for rapid quantitative mineral mapping which requires only the spectrum of the mineral to be mapped. A priori knowledge of background spectral signatures is not required. Our investigation applies CEM to calibrated radiance data converted to apparent reflectance (AR) and to single scattering albedo (SSA) spectra. The radiance data were acquired by the 210 channel, 0.4 micrometers to 2.5 micrometers airborne Hyperspectral Digital Imagery Collection Experiment sensor. CEM applied to AR spectra assumes linear mixing of the spectra of the materials exposed at the surface. This assumption is likely invalid as surface materials, which are often mixtures of particulates of different substances, are more properly modeled as intimate mixtures and thus spectral mixing analyses must take account of nonlinear effects. One technique for approximating nonlinear mixing requires the conversion of AR spectra to SSA spectra. The results of CEM applied to SSA spectra are compared to those of CEM applied to AR spectra. The occurrence of alunite is similar though not identical to mineral maps produced with both the SSA and AR spectra. Alunite is slightly more widespread based on processing with the SSA spectra. Further, fractional abundances derived from the SSA spectra are, in general, higher than those derived from AR spectra. Implications for the interpretation of quantitative mineral mapping with hyperspectral remote sensing data are discussed.

Paper Details

Date Published: 6 November 1996
PDF: 11 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257168
Show Author Affiliations
Ronald G. Resmini, Science Applications International Corp. (United States)
William R. Graver, Science Applications International Corp. (United States)
Mary E. Kappus, Naval Research Lab. (United States)
Mark E. Anderson, SPAWAR-43 (United States)

Published in SPIE Proceedings Vol. 2821:
Hyperspectral Remote Sensing and Applications
Sylvia S. Shen, Editor(s)

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