Next Article in Journal
Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment
Previous Article in Journal
Raman Microspectroscopy of Individual Algal Cells: Sensing Unsaturation of Storage Lipids in vivo
Article Menu

Export Article

Open AccessArticle
Sensors 2010, 10(9), 8652-8662;

Predicting the Detectability of Thin Gaseous Plumes in Hyperspectral Images Using Basis Vectors

Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA
Author to whom correspondence should be addressed.
Received: 17 July 2010 / Revised: 13 August 2010 / Accepted: 19 August 2010 / Published: 17 September 2010
(This article belongs to the Section Chemical Sensors)
Full-Text   |   PDF [558 KB, uploaded 21 June 2014]   |  


This paper describes a new method for predicting the detectability of thin gaseous plumes in hyperspectral images. The novelty of this method is the use of basis vectors for each of the spectral channels of a collection instrument to calculate noise-equivalent concentration-pathlengths instead of matching scene pixels to absorbance spectra of gases in a library. This method provides insight into regions of the spectrum where gas detection will be relatively easier or harder, as influenced by ground emissivity, temperature contrast, and the atmosphere. Our results show that data collection planning could be influenced by information about when potential plumes are likely to be over background segments that are most conducive to detection. View Full-Text
Keywords: plume; detection; LWIR; basis vectors; NECL plume; detection; LWIR; basis vectors; NECL

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Anderson, K.K.; Tardiff, M.F.; Chilton, L.K. Predicting the Detectability of Thin Gaseous Plumes in Hyperspectral Images Using Basis Vectors. Sensors 2010, 10, 8652-8662.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top