Dr. Fengzhen Huang Profile

Dr. Fengzhen Huang

at Beihang Univ

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Publications (6)

SPIE Journal Paper | 12 January 2017

Beam steering uncertainty analysis for Risley prisms based on Monte Carlo simulation

Hao Zhang, Yan Yuan, Lijuan Su, Fengzhen Huang

OE, Vol. 56, Issue 01, 014105, (January 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.1.014105

KEYWORDS: Interfaces, Prisms, Beam steering, Error analysis, Monte Carlo methods, Uncertainty analysis, Sodium, LIDAR, Optical engineering, Image quality

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Proceedings Article | 27 September 2016 Paper

General model for the pointing error analysis of Risley-prism system based on ray direction deviation in light refraction

Hao Zhang, Yan Yuan, Lijuan Su, Fengzhen Huang, Qing Bai

Proceedings Volume 9684, 96842B (2016) https://doi.org/10.1117/12.2241155

KEYWORDS: Error analysis, Refraction, Prisms, Instrument modeling, Ray tracing, Interfaces, Beam steering, Systems modeling, Refractive index, LIDAR

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SPIE Journal Paper | 7 May 2015

Modeling images with motion error scanned by a temporally and spatially modulated Fourier-transform imaging spectrometer

Fengzhen Huang, Jingzhen Li, Jun Cao

JARS, Vol. 9, Issue 01, 096056, (May 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096056

KEYWORDS: Imaging systems, Motion models, Image processing, Modulation, Spectroscopy, Lithium, Sensors, Image registration, Data acquisition, Remote sensing

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Proceedings Article | 19 February 2015 Paper

Spectrum recovery method analysis on nonuniform sampling interference data

Fengzhen Huang, Jingzhen Li, Jun Cao

Proceedings Volume 9449, 94490X (2015) https://doi.org/10.1117/12.2078130

KEYWORDS: Nonuniform sampling, Fourier transforms, Detection and tracking algorithms, Spectroscopy, Target detection, Imaging systems, Target acquisition, Remote sensing, Mirrors, Modulation

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Proceedings Article | 19 February 2015 Paper

Interferogram interpolation method research on TSMFTIS based on kernel regression with relative deviation

Fengzhen Huang, Jingzhen Li, Jun Cao

Proceedings Volume 9449, 944932 (2015) https://doi.org/10.1117/12.2078124

KEYWORDS: Target detection, Image restoration, Image processing, Imaging systems, Fourier transforms, Spectroscopy, Remote sensing, Image interpolation, Modulation, Sensors

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Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer (TSMFTIS) is a new imaging spectrometer without moving mirrors and slits. As applied in remote sensing, TSMFTIS needs to rely on push-broom of the flying platform to obtain the interferogram of the target detected, and if the moving state of the flying platform changed during the imaging process, the target interferogram picked up from the remote sensing image sequence will deviate from the ideal interferogram, then the target spectrum recovered shall not reflect the real characteristic of the ground target object. Therefore, in order to achieve a high precision spectrum recovery of the target detected, the geometry position of the target point on the TSMFTIS image surface can be calculated in accordance with the sub-pixel image registration method, and the real point interferogram of the target can be obtained with image interpolation method. The core idea of the interpolation methods (nearest, bilinear and cubic etc) are to obtain the grey value of the point to be interpolated by weighting the grey value of the pixel around and with the kernel function constructed by the distance between the pixel around and the point to be interpolated. This paper adopts the gauss-based kernel regression mode, present a kernel function that consists of the grey information making use of the relative deviation and the distance information, then the kernel function is controlled by the deviation degree between the grey value of the pixel around and the means value so as to adjust weights self adaptively. The simulation adopts the partial spectrum data obtained by the pushbroom hyperspectral imager (PHI) as the spectrum of the target, obtains the successively push broomed motion error image in combination with the related parameter of the actual aviation platform; then obtains the interferogram of the target point with the above interpolation method; finally, recovers spectrogram with the nonuniform fast Fourier transform algorithm. Compared with the accurate spectrogram, the spectrogram recovered with the relative deviation-based kernel regression interpolation method has remarkable improvement over the previous methods.

Proceedings Article | 19 December 2013 Paper

Wavelet basis spectrum recovery for nonuniformly sampled interferogram

Xiaoming Ding, Yan Yuan, Lijuan Su, Fengzhen Huang

Proceedings Volume 9045, 90450S (2013) https://doi.org/10.1117/12.2037673

KEYWORDS: Wavelets, Fourier transforms, Spectrometers, Error analysis, Modulation, Discrete wavelet transforms, Spectroscopy, Nonlinear optics, Environmental sensing, Opto mechatronics

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