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Proceedings Paper

Modeling precision and accuracy of a LWIR microgrid array imaging polarimeter
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Paper Abstract

Long-wave infrared (LWIR) imaging is a prominent and useful technique for remote sensing applications. Moreover, polarization imaging has been shown to provide additional information about the imaged scene. However, polarization estimation requires that multiple measurements be made of each observed scene point under optically different conditions. This challenging measurement strategy makes the polarization estimates prone to error. The sources of this error differ depending upon the type of measurement scheme used. In this paper, we examine one particular measurement scheme, namely, a simultaneous multiple-measurement imaging polarimeter (SIP) using a microgrid polarizer array. The imager is composed of a microgrid polarizer masking a LWIR HgCdTe focal plane array (operating at 8.3-9.3 μm), and is able to make simultaneous modulated scene measurements. In this paper we present an analytical model that is used to predict the performance of the system in order to help interpret real results. This model is radiometrically accurate and accounts for the temperature of the camera system optics, spatial nonuniformity and drift, optical resolution and other sources of noise. This model is then used in simulation to validate it against laboratory measurements. The precision and accuracy of the SIP instrument is then studied.

Paper Details

Date Published: 18 August 2005
PDF: 12 pages
Proc. SPIE 5888, Polarization Science and Remote Sensing II, 58880U (18 August 2005); doi: 10.1117/12.613658
Show Author Affiliations
James K. Boger, Univ. of New Mexico (United States)
J. Scott Tyo, Univ. of New Mexico (United States)
Bradley M. Ratliff, Applied Technology Associates (United States)
Matthew P. Fetrow, Air Force Research Lab. (United States)
Wiley T Black, Applied Technology Associates (United States)
Rakesh Kumar, Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 5888:
Polarization Science and Remote Sensing II
Joseph A. Shaw; J. Scott Tyo, Editor(s)

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