Gas plume species identification by 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 2007-06-22T15:09:32Z en_US
dc.date.available 2007-06-22T15:09:32Z en_US
dc.date.issued 2004-04 en_US
dc.identifier.citation Proceedings of SPIE Sensor Data Exploitation and Target Recognition, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X 5425 (2004) 583-591 en_US
dc.identifier.issn 0277-786X en_US
dc.identifier.uri http://hdl.handle.net/1850/4062 en_US
dc.description "Gas plume species identification by regression analyses," Proceedings of the SPIE, Sensor Data Exploitation and Target Recognition, Algorithms, and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Vol. 5425. The International Society of Optical Engineers. Held in Orlando, Florida: April 2004. 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. Overhead 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 will then be used only on pixels having been determined to contain the plume. Stepwise linear regression is considered in this study. Stepwise regression is attractive for this application as only those gases truly in the plume will be present in the final model. Preliminary results from the study show that stepwise regression is successful at correctly identifying the gases present in a plume. Analysis of the results indicates that the spectral overlap of absorption features in different gas species leads to false identifications. 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 en_US
dc.language.iso en_US en_US
dc.publisher The International Society for Optical Engineering (SPIE) en_US
dc.relation.ispartofseries vol. 5425 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 by regression analyses en_US
dc.type Article en_US

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