Parametric prediction of the POD and PFA for reflective hyperspectral imaging systems: dependencies on target, scene and sensor design characteristics, and detection algorithms

Edward M. Bassett; Terrence S. Lomheim; Jeffrey A. Lang; Thomas L. Hayhurst

doi:10.1117/12.683279

1 September 2006 Parametric prediction of the POD and PFA for reflective hyperspectral imaging systems: dependencies on target, scene and sensor design characteristics, and detection algorithms

Edward M. Bassett, Terrence S. Lomheim, Jeffrey A. Lang, Thomas L. Hayhurst

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Proceedings Volume 6302, Imaging Spectrometry XI; 63020B (2006) https://doi.org/10.1117/12.683279
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

Advanced ground and space-based hyperspectral imager (HSI) concepts are being developed for a wide variety of scientific, civil, and military applications. Users and developers of these systems often require the specification of system performance in terms of receiver-operator characteristic (ROC) curves which plot probability-of-detection (POD) versus probability-of-false-alarm (PFA). In this paper we describe and illustrate the use of a scene-based modeling tool used to explore ROC curve parametric dependencies on target, scene, and HSI sensor design characteristics and detection algorithms in the visible/near infrared to shortwave infrared (VNIR/SWIR) spectral regime (i.e. from 0.4 to 2.5 microns). The magnitudes of the target and background spectral signatures are synthesized using MODTRAN; this accounts for pertinent solar elevation angle and albedo assumptions. Selected spectral input scenes (based on measured data) are used assuming imbedded spectral targets (selectable), where a fill-factor parameter is used to account for target dimension compared to sensor ground footprint. The HSI sensor sensitivity characteristics are imbedded via the noise-equivalent reflectivity difference (NE▵ρ) figure-of-merit which is computed spectrally based on a given sensor design configuration. Finally the POD, PFA and hence ROC parametrics are generated using a distinct candidate detection algorithm. The roles of scene clutter, illumination conditions, and sensor signal-to-noise ratio are made clear in simulation examples. In addition the impact of limited scene extent (limited scene pixel count) on the accuracy of the PFA predictions is noted and discussed.

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Edward M. Bassett, Terrence S. Lomheim, Jeffrey A. Lang, and Thomas L. Hayhurst "Parametric prediction of the POD and PFA for reflective hyperspectral imaging systems: dependencies on target, scene and sensor design characteristics, and detection algorithms", Proc. SPIE 6302, Imaging Spectrometry XI, 63020B (1 September 2006); https://doi.org/10.1117/12.683279

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KEYWORDS

Sensors

Target detection

Reflectivity

Detection and tracking algorithms

Atmospheric modeling

Hyperspectral target detection

Signal to noise ratio

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