Spectral quality equation relating collection parameters to material identification performance

Sylvia S. Shen

doi:10.1117/12.604493

1 June 2005 Spectral quality equation relating collection parameters to material identification performance

Sylvia S. Shen

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.604493
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

A methodology capable of quantitatively assessing the quality of hyperspectral data has become increasingly desirable as hyperspectral remote sensing technology migrates into operational systems. The quality of spectral data depends on many factors including collection parameters charactering the sensor and the scene, and the desired spectral products. Therefore, there is a recognized urgent need to understand the phenomenology associated with the collection paramters and how they relate to the quality of the information extracted from the spectral data for different applications. If such relationships can be established, data collection requirements and tasking strategies can then be formulated for these applications. A spectal quality equation with an excellent least-squares fit was established for object/anomaly detection in an earlier work. This paper describes a spectral quality equation established for material identification. This spectral quality equation relates the collection parameters (i.e. spatial resolution, spectral resolution, signal-to-noise ratio, and scene complexity) to the probability of correct identification (Pi) of materials at a given probability of false alarms (Pfa).

Citation Download Citation

Sylvia S. Shen "Spectral quality equation relating collection parameters to material identification performance", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.604493

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KEYWORDS

Signal to noise ratio

Spatial resolution

Spectral resolution

Quality measurement

Sensors

Target detection

Palladium

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