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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 46, Iss. 25 — Sep. 1, 2007
  • pp: 6368–6374

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

Eitan Hirsch and Eyal Agassi  »View Author Affiliations


Applied Optics, Vol. 46, Issue 25, pp. 6368-6374 (2007)
http://dx.doi.org/10.1364/AO.46.006368


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Abstract

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.

© 2007 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(280.1120) Remote sensing and sensors : Air pollution monitoring

ToC Category:
Remote Sensing and Sensors

History
Original Manuscript: March 20, 2007
Revised Manuscript: June 29, 2007
Manuscript Accepted: July 2, 2007
Published: August 27, 2007

Citation
Eitan Hirsch and Eyal Agassi, "Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering," Appl. Opt. 46, 6368-6374 (2007)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-46-25-6368

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