
Proceedings Paper
A novel automated object identification approach using key spectral componentsFormat | Member Price | Non-Member Price |
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Paper Abstract
Spectral remote sensing provides solutions to a wide range of commercial, civil,
agricultural, atmospheric, security, and defense problems. Technological advances have
expanded multispectral (MSI) and hyperspectral (HSI) sensing capabilities from air and space
borne sensors. The greater spectral and spatial sensitivity have vastly increased the available
content for analysis. The amount of information in the data cubes obtained from today’s sensors
enable material identification via complex processing techniques. With sufficient sensor resolution,
multiple pixels on target are obtained and by exploiting the key spectral features of a material
signature among a group of target pixels and associating the features with neighboring pixels,
object identification is possible. The authors propose a novel automated approach to object
classification with HSI data by focusing on the key components of an HSI signature and the
relevant areas of the spectrum (bands) of surrounding pixels to identify an object. The proposed
technique may be applied to spectral data from any region of the spectrum to provide object
identification. The effort will focus on HSI data from the visible, near-infrared and short-wave
infrared to prove the algorithm concept.
Paper Details
Date Published: 5 June 2013
PDF: 12 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430L (5 June 2013); doi: 10.1117/12.2016620
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 12 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430L (5 June 2013); doi: 10.1117/12.2016620
Show Author Affiliations
Bart Kahler, SAIC (United States)
Todd Noble, SAIC (United States)
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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