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Proceedings Paper

Microscene evaluation using the Bhattacharyya distance
Author(s): William F. Basener; Marty Flynn
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Paper Abstract

A microscene is a hyperspectral image collected using a hyperspectral sensor mounted above a tray, typically in a laboratory setting. Materials can be placed in the tray and illumination controlled to either analyze the materials used or to simulate overhead (aerial or satellite) imagery. Choosing the materials allows simulation of overhead imagery in controlled experiments, for example mixtures and abundances of chemicals, materials as they undergo physical and chemical processes such as oxidation and weathering, and vegetation at different stages in environmental processes. Microscene imagery enables experiments in controlled circumstances not easily producible in overhead imagery. Moreover, the cost of collecting microscene imagery is a small fraction of overhead collection. Microscene imagery is an emerging technology, and in this paper we address an evaluation microscene imagery to determine how well it simulates overhead imagery, comparing microscene imagery of vegetation to overhead AVIRIS and HYDICE imagery over vegetation. We use statistical measures to compare microscene imagery to overhead imagery, including comparing material spectra, means, eigenvalues, the Mahalanobis distance between image means, and for the first time the Bhattacharyya distance between image covariances. The Bhattacharyya is a statistical measure of the distance between two statistical distributions, related to the Mahalanobis distance.

Paper Details

Date Published: 23 October 2018
PDF: 13 pages
Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 107800R (23 October 2018); doi: 10.1117/12.2327004
Show Author Affiliations
William F. Basener, Spectral Solutions (United States)
Univ. of Virginia (United States)
Rochester Institute of Technology (United States)
Marty Flynn, U.S. Army (United States)

Published in SPIE Proceedings Vol. 10780:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII
Allen M. Larar; Makoto Suzuki; Jianyu Wang, Editor(s)

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