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Proceedings Paper

Separability of VNIR/SWIR reflectance signatures of prepared soil samples: airborne hyperspectral vs. field measurements
Author(s): Jerrold E. Baum; Seth Orloff; Su May Hsu; Hsiao-hua Burke
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Paper Abstract

To better understand the capabilities of hyperspectral imaging spectrometers, a number of organizations planned and carried out a data collection exercise at a desert site in the southwestern United States. As part of this collection, eight soil 'panels' were constructed; four filled with a coarse gravel/sand mixture and four flled with fine soil. Each set of four panels was prepared to represent two moisture and density conditions: wet versus dry and compacted versus loose. Unlike laboratory soil specimens, which use 'purified' samples, these soil flats contained more variability. They therefore better represented the 'natural' environment that would be viewed by an airborne hyperspectral imaging sensor, while still allowing an experimental study under more controlled conditions. This paper examines how well the eight soil types and conditions can be distinguished based on their VNIR/SWIR reflectance spectra derived from field measurements and from airborne hyperspectral measurements made at nearly the same time. A brief review of the phenomenology of soil reflectance spectra will be given. Based on physical attributes of the soils, some new classification approaches have been developed and were applied to the soil panels. These phenomenological methods include examining contrast in certain broadband features and, based on these, calculating various broadband spectral ratios over subsets of the VNIR/SWIR spectral region. The separability of the reflectance spectra from the eight soil panels were also analyzed by applying the Spectral Angle Mapper (SAM) hyperspectral distance metric to quantify the separations between all pairs of soil types and conditions. Finally, a neural network approach was applied to determine distinguishing features of the spectra. The phenomenological approaches, SAM analyses, and the neural network results will be compared.

Paper Details

Date Published: 7 January 2004
PDF: 12 pages
Proc. SPIE 5159, Imaging Spectrometry IX, (7 January 2004); doi: 10.1117/12.504598
Show Author Affiliations
Jerrold E. Baum, MIT Lincoln Lab. (United States)
Seth Orloff, MIT Lincoln Lab. (United States)
Su May Hsu, MIT Lincoln Lab. (United States)
Hsiao-hua Burke, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 5159:
Imaging Spectrometry IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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