Polarimetric microfacet scattering theory with applications to absorptive and reflective surfaces

Priest, R. G.; Meier, Steven R.

doi:10.1117/1.1467360

Cited by 126 publications

(35 citation statements)

References 14 publications

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“…The BRDF is taken to be the sum of a depolarizing modified Rahman-PintyVerstraete (mRPV) model polarizing term generated by an array of Fresnel-reflecting microfacets (Priest and Meier, 2002). The refractive index of the surface, n r,surf , is taken to be 1.5 (Waquet et al, 2009); however, in contrast to the ocean model, a wavelengthindependent factor, ζ , multiplies the magnitude of the microfacet contribution to the BRDF to compensate for a possibly incorrect choice of n r,surf .…”

Section: Hazy Sky Observations Over Landmentioning

confidence: 99%

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

et al. 2013

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Abstract. The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is an eight-band (355, 380, 445, 470, 555, 660, 865, 935 nm) pushbroom camera, measuring polarization in the 470, 660, and 865 nm bands, mounted on a gimbal to acquire multiangular observations over a ±67° along-track range. The instrument has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. AirMSPI employs a photoelastic modulator-based polarimetric imaging technique to enable accurate measurements of the degree and angle of linear polarization in addition to spectral intensity. A description of the AirMSPI instrument and ground data processing approach is presented. Example images of clear, hazy, and cloudy scenes over the Pacific Ocean and California land targets obtained during flights between 2010 and 2012 are shown, and quantitative interpretations of the data using vector radiative transfer theory and scene models are provided to highlight the instrument's capabilities for determining aerosol and cloud microphysical properties and cloud 3-D spatial distributions. Sensitivity to parameters such as aerosol particle size distribution, ocean surface wind speed and direction, cloud-top and cloud-base height, and cloud droplet size is discussed. AirMSPI represents a major step toward realization of the type of imaging polarimeter envisioned to fly on NASA's Aerosol-Cloud-Ecosystem (ACE) mission in the next decade.

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Section: Hazy Sky Observations Over Landmentioning

confidence: 99%

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

et al. 2013

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“…which are mathematically equivalent to formulas given in [42] and [49]. From the definition of PBRDF, the reflected Stokes vector from the surface is given by…”

Section: Groundmspi Imagerymentioning

confidence: 99%

Exploration of a Polarized Surface Bidirectional Reflectance Model Using the Ground-Based Multiangle SpectroPolarimetric Imager

Diner

Martonchik

et al. 2012

Atmosphere

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Accurate characterization of surface reflection is essential for retrieval of aerosols using downward-looking remote sensors. In this paper, observations from the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI) are used to evaluate a surface polarized bidirectional reflectance distribution function (PBRDF) model. GroundMSPI is an eight-band spectropolarimetric camera mounted on a rotating gimbal to acquire pushbroom imagery of outdoor landscapes. The camera uses a very accurate photoelastic-modulator-based polarimetric imaging technique to acquire Stokes vector measurements in three of the instrument's bands (470, 660, and 865 nm). A description of the instrument is presented, and observations of selected targets within a scene acquired on 6 January 2010 are analyzed. Data collected during the course of the day as the Sun moved across the sky provided a range of illumination geometries that facilitated evaluation of the OPEN ACCESSAtmosphere 2012, 3 592 surface model, which is comprised of a volumetric reflection term represented by the modified Rahman-Pinty-Verstraete function plus a specular reflection term generated by a randomly oriented array of Fresnel-reflecting microfacets. While the model is fairly successful in predicting the polarized reflection from two grass targets in the scene, it does a poorer job for two manmade targets (a parking lot and a truck roof), possibly due to their greater degree of geometric organization. Several empirical adjustments to the model are explored and lead to improved fits to the data. For all targets, the data support the notion of spectral invariance in the angular shape of the unpolarized and polarized surface reflection. As noted by others, this behavior provides valuable constraints on the aerosol retrieval problem, and highlights the importance of multiangle observations.

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“…where denotes the element in the row and column of the pBRDF Mueller matrix F, denotes the element in the row and column of the Fresnel reflectance Mueller matrix M, is the angle of orientation of the microfacets relative to the object surface normal, is given by = and describes the surface roughness [10]. It is the parameter that we are interested in estimating here.…”

Section: Polarimetric Bidirectional Reflectance Distribution Functionmentioning

confidence: 98%

“…This can be easily accomplished from an aerial platform moving towards a target object by logging GPS locations and time stamps during each capture and correlating these to the ground position using mapping software. It is assumed that each of the captured polarimetric images is generated by the same underlying model-in this case, the microfacet pBRDF model for specular reflection proposed by Priest and Meier [10]-and that by solving for the parameters which best fit this model, one can make progressively better estimates for each image pixel. Figure 1 shows the geometry assumed for the estimation process and the associated polarimetric BRDF is given by,…”

Section: Polarimetric Bidirectional Reflectance Distribution Functionmentioning

confidence: 99%

Model-based estimation of surface geometry using passive polarimetric imaging

Creusere

Mehta

Voelz

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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Imaging polarimetry has emerged as a powerful tool for application in the field of remote sensing. In this paper, we present a novel technique for estimating the surface normal angle of each of the individual facets of a target object using passive polarimetric data. The passive polarimetric imaging system described here uses multiple measurements of the output Stokes vectors along with the reflection Mueller matrix, to extract the surface normal angle corresponding to individual facets of the target object. The knowledge of this parameter is indispensable for determining the orientation and surface geometry of the target object and thus facilitates applications like object recognition, shape extraction and building scene geometry. The worst-case error is found to be less than 2%, based on Monte Carlo computer simulation results.

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Polarimetric microfacet scattering theory with applications to absorptive and reflective surfaces

Cited by 126 publications

References 14 publications

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

Exploration of a Polarized Surface Bidirectional Reflectance Model Using the Ground-Based Multiangle SpectroPolarimetric Imager

Model-based estimation of surface geometry using passive polarimetric imaging

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