Material Classification of an Unknown Object Using Turbulence-Degraded Polarimetric Imagery

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

doi:10.1109/tgrs.2010.2053547

Cited by 38 publications

(11 citation statements)

References 53 publications

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“…We note that our model and estimation approach is general and other scattering models [30] can be incorporated in the pBRDF expressions. The approach can also be extended to include a partially polarized diffuse scattering component, compensate for atmospheric turbulence effects [31][32], or introduce illumination properties due to cloudy/overcast conditions [33] to improve the parameter estimation accuracy.…”

Section: Model Developmentmentioning

confidence: 99%

Parameter-based imaging from passive multispectral polarimetric measurements

2019

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A modified pBRDF model with a diffuse scattering component is applied to estimate the complex refractive index, slope variance roughness, and diffuse scattering coefficients of object surfaces from time sequences of polarimetric images. The approach is used for the first time to produce parameter-based images from multispectral Stokes imagery of outdoor target scenes collected by the Ground Multiangle Spectro-Polarimetric Imager. The images of the estimated surface parameters show distinctions between different objects in the scenes and the parameter values are consistent with reasonable expectations for the object surfaces.

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Section: Model Developmentmentioning

confidence: 99%

Parameter-based imaging from passive multispectral polarimetric measurements

2019

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“…By this way, the information we can get includes not only traditional radiation attributes (i.e., intensity and spectrum), but also polarization parameters such as degree of polarization, angle of polarization, degree of linear polarization and so on [3]. Polarization parameters can be used for contrast enhancing in target detection [4], man-made object imaging [5] and material classification [6].…”

Section: Introductionmentioning

confidence: 99%

Influence of surface roughness on polarization property in passive millimeter-wave imaging

Zhang

Cheng

et al. 2017

IEICE Electron. Express

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Polarimetric measurement can gain more object information when compared to traditional methods. Linear polarization ratio (LPR) of the millimeter-wave thermal emission has been presented recently and proved to be effective in material classification. The LPR classification technique can be used for the metal detection in the soil and concrete ground. The roughness has not been considered in analysing LPR properties and it may affect the classification performance. In this paper, we focus on the influence of surface roughness on LPR. By solving scattering problem, the LPR properties of different roughness parameters are investigated. Theoretical calculations indicate that the LPR value decreases with the increasing of surface roughness. In addition, the applicable scope of LPR classification technique to discriminate rough surfaces is discussed.

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“…Many algorithms have been developed for supervised and unsupervised terrain classification [3][4][5]. The complex Wishart classifier for multi-look PolSAR data [6] is the most commonly used supervised classification method.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric calibration requirements on several classification schemes for land application of polarimetric synthetic aperture radar

Wang

et al. 2013

IET Radar, Sonar & Navigation

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Polarimetric calibration intercalibrating the polarisation channels of the radar system results in a better characterisation of the target and of the backscattering mechanisms. However, system distortion because of miscalibration may affect the classification performance to some extent. The study described in this article is an attempt at deriving the polarimetric calibration requirements on several algorithms for the classification of land features. In the monostatic backscattering case, the model representing the effects of the residual distortion of the calibrated polarimetric data is presented first, followed by analysing the effects of miscalibration on a few commonly used polarimetric parameters and several classification schemes. Experimental results show that polarimetric calibration requirements not only are strongly dependent on classification methods but also directly relate to the underlying physical scattering mechanisms of the observables. For each classification algorithm under study, a set of numbers summarising the polarimetric calibration goals concludes this study.

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Material Classification of an Unknown Object Using Turbulence-Degraded Polarimetric Imagery

Cited by 38 publications

References 53 publications

Parameter-based imaging from passive multispectral polarimetric measurements

Parameter-based imaging from passive multispectral polarimetric measurements

Influence of surface roughness on polarization property in passive millimeter-wave imaging

Polarimetric calibration requirements on several classification schemes for land application of polarimetric synthetic aperture radar

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