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Proceedings Paper

Spectral effluent detection sensitivity study
Author(s): Steve T. Kacenjar; Davina F. Gill; John A. Lelii; Jack Foreman; Cynthia B. Batroney
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Paper Abstract

The release of harmful effluents into the earth's atmosphere is an increasing world-wide concern. Technical feasibility to detect and localize such releases is a necessary first step before sound policy decisions can be established to regulate such releases. This paper examines key system parameters by quantifying their effects in terms of Receiver Operating Characteristics (ROC) curves. It establishes upper performance bounds based on perfect apriori information of the atmospheric state and thus can be used to gauge measured effectiveness of candidate detection algorithms. In the first part of this paper, a theoretical discussion is presented on the development of the probability density functions (pdfs) required to perform the ROC analysis. These pdfs are associated with 12 measures of spectral vector magnitude lengths, a measurable quantity for effluent detection. It will be shown that these functions are non-Gaussian and are functions of the number of spectral bands in the data. Generation of these functions are also discussed in this paper both analytical and through Monte Carlo methods. In the second half of this paper, ROC performance curves are generated for various sensor and source parameters. These curves are generated for various sensor noise and spectral aggregation conditions constrained by the assumption of shot-limited performance. Impacts of spectral aggregation and plume-to- ground temperature differentials are also examined and related to ROC performance. Parametric evaluations are confined to the long-wave infrared (LWIR) spectral regime where sensor resolution is systematically varied between 1.0 and 8.0 cm-1. System models, analysis methodology, and ROC results are presented.

Paper Details

Date Published: 2 July 1998
PDF: 12 pages
Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312599
Show Author Affiliations
Steve T. Kacenjar, Lockheed Martin Management and Data Systems (United States)
Davina F. Gill, Lockheed Martin Management and Data Systems (United States)
John A. Lelii, Lockheed Martin Management and Data Systems (United States)
Jack Foreman, Lockheed Martin Management and Data Systems (United States)
Cynthia B. Batroney, Lockheed Martin Management and Data Systems (United States)


Published in SPIE Proceedings Vol. 3372:
Algorithms for Multispectral and Hyperspectral Imagery IV
Sylvia S. Shen; Michael R. Descour, Editor(s)

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