Gas plume species identification by regression analyses

David R. Pogorzala; David W. Messinger; Carl Salvaggio; John R. Schott

doi:10.1117/12.541641

12 August 2004 Gas plume species identification by regression analyses

David R. Pogorzala, David W. Messinger, Carl Salvaggio, John R. Schott

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.541641
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

David R. Pogorzala, David W. Messinger, Carl Salvaggio, and John R. Schott "Gas plume species identification by regression analyses", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.541641

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available