Determining the complex index of refraction of an unknown object using turbulence-degraded polarimetric imagery

Hyde, Milo W.; Schmidt, Jason D.; Havrilla, Michael J.; Cain, Stephen C.

doi:10.1117/1.3518044

Cited by 7 publications

(10 citation statements)

References 51 publications

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“…The spectroradiometer used in the following experiment has a spectral resolution of 1 nm and spectral range from 350nm to 2500nm, which is applicable for data collection. Besides, compared with previous methods that depend on multi-angle measurements for estimation [14], [15], the presented method is helpful for avoiding the direction measurement error caused by changing angles in experiment analysis and the image shift brought by multi-angle polarization imaging in passive remote sensing application.…”

Section: Simulationmentioning

confidence: 99%

“…However, they did not take into account the law that complex refractive index changes with the wavelength. As a result, these models can only use Hyde et al [15] proposed a method to estimate the complex index of refraction of an unknown object from multiple-angle polarimeter measurements. In order to improve the estimating accuracy, they developed a blind-deconvolution algorithm to correct measured degree of linear polarization (DOLP) until it fits the DOLP priors.…”

Section: Introductionmentioning

confidence: 99%

“…),(11),(12), the DOLP equation which contains the diffuse component of pBRDF model is derived from the pBRDF model[15] as:This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

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confidence: 99%

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Modified Degree of Polarization Function for Rough Metallic Surface Parameter Estimation Based on Multispectral Polarimetric Measurement

Wang

Yuan

et al. 2020

IEEE Access

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In order to recognize different materials from passive polarimetric measurements specifically, a model that can describe the distribution of polarization energy scattered from the target surface is needed for estimating the parameters of interest (complex refractive index n, k and surface roughness ) of targets. This paper presents a new method for targets parameters estimation. The presented method modifies the degree of linear polarization (DOLP) model by introducing the Lorentz-Drude dispersion constants which do not vary with wavelength as well as the diffuse component of reflection related to the surface roughness to the DOLP equation. This solves the problem of difficulty and inaccuracy caused by multi-angle passive polarimetric measurements used by previous works and helps to improving the parameters estimation accuracy for rough targets. The Monte Carlo simulations result illustrate that the presented method is robust to Gaussian noise. Moreover, the experimental results of rough aluminium plate and rough copper plate validate the effectiveness of the presented model. The errors of estimated n are about 8% for aluminium plate and 9% for copper plate. The errors of estimated k are less than 4% for aluminium plate and no more than 5% for copper plate. The errors of estimated surface roughness  are about 6.8% and about 6% for aluminium plate and copper plate respectively. The high estimation accuracy and less detection angle requirement achieved by the presented method are significant for material characterization in passive remote sensing application.

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified Degree of Polarization Function for Rough Metallic Surface Parameter Estimation Based on Multispectral Polarimetric Measurement

Wang

Yuan

et al. 2020

IEEE Access

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“…More sophisticated material classification techniques using passive polarimetric imagers have also been investigated to classify material types. [7][8][9][10][11] In order to obtain successful results, the techniques must make several assumptions such as a known orientation of the material surface and a single illumination source. These assumptions may not be valid in actual remote sensing applications utilizing passive sensors alone.…”

Section: Introductionmentioning

confidence: 99%

Hybrid passive polarimetric imager and lidar combination for material classification

et al. 2020

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We investigate the augmentation of active imaging with passive polarimetric imaging for material classification. Experiments are conducted to obtain a multimodal dataset of lidar reflectivity and polarimetric thermal self-emission measurements against a diverse set of material types. Using the assumption that active lidar imaging can provide high-resolution threedimensional spatial information, a known surface orientation is utilized to enable higher fidelity classification. Machine learning is applied to the dataset of monostatic lidar unidirectional reflectivity and passive longwave infrared degree of linear polarization features for material classification. The hybrid sensor technique can classify materials with 91.1% accuracy even with measurement noise resulting in a signal-to-noise ratio of only 6 dB. The application of the proposed technique is applicable for the classification of hidden objects or could assist existing spatial-based object classification. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Accordingly, AFIT has procured a flexible AO system from SAIC that is appropriate for initial testing of new algorithms and transitioning to real-time operation with live field tests. Portions of the system have already been used for graduate laboratory classes and investigations of polarimetric remote sensing [24][25][26] and modeling extend beacons. 18…”

Section: Introductionmentioning

confidence: 99%

A flexible testbed for adaptive optics in strong turbulence

Schmidt¹,

Steinbock²,

Berg

2011

SPIE Proceedings

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In recent years, optical wave propagation through strong atmospheric turbulence and adaptive optics compensation thereof has received much attention in literature and technical meetings. At the Air Force Institute of Technology, recent simulation-based efforts in strong turbulence compensation are expanding into laboratory experiments utilizing a versatile surrogate turbulence simulator and adaptive optics system. The system can switch between using two different wavefront sensors, a Shack-Hartmann and a self-referencing interferometer. Wavefront reconstruction takes place on field programmable gate arrays, operating at kilohertz frame rates. Further, the system is able to perform reconstruction and control in software for testing of advanced algorithms (at frame rates below 10 Hz). The entire package is compact enough for transportation to other laboratories and live test facilities. This paper describes the optical layout, architecture, and initial results of real-time operation.

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Determining the complex index of refraction of an unknown object using turbulence-degraded polarimetric imagery

Cited by 7 publications

References 51 publications

Modified Degree of Polarization Function for Rough Metallic Surface Parameter Estimation Based on Multispectral Polarimetric Measurement

Modified Degree of Polarization Function for Rough Metallic Surface Parameter Estimation Based on Multispectral Polarimetric Measurement

Hybrid passive polarimetric imager and lidar combination for material classification

A flexible testbed for adaptive optics in strong turbulence

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