Display of polarization information for passive millimeter-wave imagery

Wilson, John P.

doi:10.1117/1.oe.51.9.091607

Cited by 19 publications

(2 citation statements)

References 25 publications

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“…Polarimetric images provide information based on the polarization signature of radiation instead of detecting optical radiation with scalar fields in traditional infrared images [ 6 , 7 ]. The polarization signature depends on the polarization properties of scattering, reflection, and absorption, which result from the geometry and roughness of the target surfaces, and it can be used to distinguish the target from the clutter and background [ 8 , 9 , 10 , 11 , 12 ]. The polarimetric imaging system usually uses a broadband imager for the spatiotemporal information with the mechanical parts of optical components, to obtain the polarimetric signature of a target.…”

Section: Introductionmentioning

confidence: 99%

Polarization-Sensitive and Wide Incidence Angle-Insensitive Fabry–Perot Optical Cavity Bounded by Two Metal Grating Layers

Hwang

Jeon

et al. 2020

Sensors

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Infrared (IR) polarimetric imaging has attracted attention as a promising technology in many fields. Generally, superpixels consisting of linear polarizer elements at different angles plus IR imaging array are used to obtain the polarized target signature by using the detected polarization-sensitive intensities. However, the spatial arrangement of superpixels across the imaging array may lead to an incorrect polarimetric signature of a target, due to the range of angles from which the incident radiation can be collected by the detector. In this article, we demonstrate the effect of the incident angle on the polarization performance of an alternative structure where a dielectric layer is inserted between the nanoimprinted subwavelength grating layers. The well-designed spacer creates the Fabry–Perot cavity resonance, and thereby, the intensity of transverse-magnetic I-polarized light transmitted through two metal grating layers is increased as compared with a single-layer metal grating, whereas transverse-electric (TE)-transmitted light intensity is decreased. TM-transmittance and polarization extinction ratio (PER) of normally incident light of wavelength 4.5 μm are obtained with 0.49 and 132, respectively, as the performance of the stacked subwavelength gratings. The relative change of the PERs for nanoimprint-lithographically fabricated double-layer grating samples that are less than 6% at an angle of incidence up to 25°, as compared to the normal incidence. Our work can pave the way for practical and efficient polarization-sensitive elements, which are useful for many IR polarimetric imaging applications.

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Section: Introductionmentioning

confidence: 99%

Polarization-Sensitive and Wide Incidence Angle-Insensitive Fabry–Perot Optical Cavity Bounded by Two Metal Grating Layers

Hwang

Jeon

et al. 2020

Sensors

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“…However, these methods usually require lighting sources. Wilson et al [16] utilized the difference of radiometric temperature for the two orthogonal linear polarization states to identify the PMMW images. In our previous works, we propose the mixed dielectric constant (MDC) to stand for the dielectric constant and offer a corresponding calculation method of MDC in passive microwave radiation related application [9].…”

Section: Introductionmentioning

confidence: 99%

Material Clustering Using Passive Millimeter-Wave Polarimetric Imagery

Tian

et al. 2019

IEEE Photonics J.

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Passive millimeter-wave imaging has emerged as a useful tool in many remote sensing applications, including resource remote sensing, material classification, and target detection. This paper presents a method to classify specular objects based on their material composition from passive millimeter-wave polarimetric imagery. Passive degree of polarization (PDoP) is proposed and calculated by using the results of a passive millimeter-wave polarization measurement. The PDoP values of typical ground targets are further analyzed. Outdoor experiments are conducted, and the PDoP image is generated from the brightness temperature images. The PDoP values of pixels in the image are statistically analyzed, and the threshold values are set based on the statistical analysis results, then every pixel is recognized. Experimental results indicate that this method is highly effective for distinguishing among various materials of interest.

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