Gas plume species identification by regression analyses

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Title: Gas plume species identification by regression analyses
Author: Pogorzala, David; Messinger, David; Salvaggio, Carl; Schott, John
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.
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.
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Date: 2004-04

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