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Proceedings Paper

Extraction of compositional information for trafficability mapping from hyperspectral data
Author(s): Fred A. Kruse; Joseph W. Boardman; Adam B. Lefkoff
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Paper Abstract

Trafficability refers to the extent to which the terrain will permit continued movement of any and/or all types of traffic, an issue that ground forces must address in advance of military operations to ensure their success. Multispectral remote sensing technology is currently used by terrain analysts to help assess trafficability, but its utility in producing classical measures of trafficabilty has been limited. This paper describes a hyperspectral trafficability mapping methodology supported by a case history using Airborne Visible/Inffared Imaging Spectrometer (AVIRIS) data. The strong points of the hyperspectral data for trafficability mapping are detection, identification, and mapping of surface composition. Selected spectral libraries were reviewed in the context of trafficability to generate classes of materials with specific trafficability characteristics. These were used in conjunction with scene-based hyperspectral analysis methodologies to produce prototype trafficability products from AVIRIS data. The AVIRIS analysis illustrates that while considerable important information regarding trafficability can be extracted from hyperspectral data, analysis of these data alone can not produce the desired “classical” trafficability products and that data fusion is required to enhance information extracted from hyperspectral data. The principal limitations of hyperspectral data, are 1. That only certain materials have unique spectral features or character that can be detected, 2. that it measures only the very surface and may not be indicative of bulk materials, and 3. That it doesn’t provide any textural information, critical for determining classical trafficability measures. Specific additional required information includes terrain information related to topography, and surface texture. This information can be obtained from supporting datasets such as high resolution digital elevation models (DEM), and Synthetic Aperture Radar (SAR).

Paper Details

Date Published: 23 August 2000
PDF: 12 pages
Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); doi: 10.1117/12.410348
Show Author Affiliations
Fred A. Kruse, Analytical Imaging and Geophysics (United States)
Joseph W. Boardman, Analytical Imaging and Geophysics (United States)
Adam B. Lefkoff, Analytical Imaging and Geophysics (United States)

Published in SPIE Proceedings Vol. 4049:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI
Sylvia S. Shen; Michael R. Descour, Editor(s)

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