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17 May 2016 Polarimetric assist to HSI atmospheric compensation and material identification
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In this effort, we investigated how polarimetric HyperSpectral Imaging (pHSI) data might benefit specified Material Identification of diffuse materials in the VNIR. The experiment compared paint reflectivities extracted from polarimetric hyperspectral data acquired in the field to a database of truth reflectivities measured in the lab. Both the polarimetric hyperspectral data and the reflectivities were acquired using an Ocean Optics spectrometer which was polarized using a fast filter wheel loaded with high extinction polarizers. During the experiment, we discovered that the polarized spectra from the polarimetric hyper spectral data could be used to estimate the relative spectral character of the field source (the exo-atmospheric sun plus the atmosphere). This benefit, which strongly parallels the QUAC atmospheric correction method, relies on the natural spectral flatness of the polarized spectrum that originates in the spectral flatness of the index of refraction in the reflective regime. Using this estimate of the field source, excellent estimates of the paint reflectivities (matching 10 paint reflectivities to ≤ 0.5% RSS) were obtained. The impact of atmospheric upwell on performance was then investigated using these ground based polarimetric hyper spectral data in conjunction with modeled atmospheric path effects. The path effects were modeled using the high fidelity Polarimetry Phenomenology Simulation (PPS) plate model developed by AFRL, which includes polarized Modtran. We conclude with a discussion of actual and potential applications of this method, and how best to convert an existing VNIR HSI sensor into a pHSI sensor for an airborne Proof Of Concept experiment.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Gibney "Polarimetric assist to HSI atmospheric compensation and material identification", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401P (17 May 2016);

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