Gas plume species identification in airborne LWIR imagery using constrained stepwise regression analyses

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dc.contributor.author Pogorzala, David en_US
dc.contributor.author Messinger, David en_US
dc.contributor.author Salvaggio, Carl en_US
dc.contributor.author Schott, John en_US
dc.date.accessioned 2007-06-22T15:17:38Z en_US
dc.date.available 2007-06-22T15:17:38Z en_US
dc.date.issued 2005-03 en_US
dc.identifier.citation Proceedings of SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI 5806 (2005) 194-205 en_US
dc.identifier.issn 0277-786X en_US
dc.identifier.uri http://hdl.handle.net/1850/4066 en_US
dc.description "Gas plume species identification in airborne LWIR imagery using constrained stepwise regression analyses," Proceedings of SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, vol. 5806. Held in Orlando, Florida: April 2005. This paper is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. en_US
dc.description.abstract Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Airborne thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work is performed only on pixels having been determined to contain the plume. Constrained stepwise linear regression is used in this study. Results indicate that the ability of the algorithm to correctly identify plume gases is directly related to the concentration of the gas. Previous concerns that the algorithm is hindered by spectral overlap were eliminated through the use of constraints on the regression. en_US
dc.description.sponsorship This work was funded under the Office of Naval Research Multi-disciplinary University Research Initiative “Model-based Hyperspectral Exploitation Algorithm Development” #N00014-01-1-0867. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Office of Naval Research. The authors would like to thank Herb Fry, Kevin Mitchell and Brian McVey for their helpful correspondence during the course of this research. en_US
dc.language.iso en_US en_US
dc.publisher The International Society for Optical Engineering (SPIE) en_US
dc.relation.ispartofseries vol. 5806 en_US
dc.subject Gaseous plumes en_US
dc.subject Hyperspectral en_US
dc.subject Stepwise regression en_US
dc.subject Target identification en_US
dc.title Gas plume species identification in airborne LWIR imagery using constrained stepwise regression analyses en_US
dc.type Article en_US

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