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Optics and Precision Engineering

Optics and Precision Engineering Optics and Precision Engineering
  • Editor-in-Chief:Xuejun Zhang, Liangcai Cao
  • ISSN:1004-924X
  • eISSN:2097-3209
  • CN:22-1198/TH
  • Supervisor:Chinese Academy of Sciences
  • Sponsor:Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China Instrument and Control Society
  • Publication frequency:Semimonthly
  • Tel.:0431-86176855
  • E-mail:gxjmgc@ciomp.ac.cn
  • Address:No.3888 Dong Nanhu Road, Changchun, Jilin, China
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Volume 34 期 11，2026 2026年第34卷第11期

  • Polarization remote sensing

    GU Haoran, LI Zhengqiang, XU Youfu, ZHANG Zihan, LI Li, MA Yan, YAO Qian

    DOI：10.37188/OPE.20263411.1639
    摘要：A retrieval method for pressure-elevation parameters based on satellite polarization imaging data is developed in this study， aiming to retrieve cloud-top pressure and reconstruct surface elevation. Based on observations from the DPC instrument onboard the GF-5（02） satellite， the sensitivity of oxygen A-band absorption to atmospheric path length is used to establish a nonlinear mapping relationship between pressure and observed signals， thereby enabling cloud-top pressure retrieval. Furthermore， by incorporating the physical relationship between atmospheric pressure and altitude， surface elevation reconstruction is achieved. Key factors affecting the retrieval process are systematically analyzed， including surface elevation errors， aerosol optical thickness， and atmospheric air mass. The results show that， in the multi-angle stability validation over the Qilian Mountains， the mean coefficient of variation of the pressure retrieval is approximately 3.98%， with overall fluctuations limited to within 8%， indicating good stability of the proposed method. In the DEM-based validation over the Asir Mountains in western Saudi Arabia， the retrieved pressure shows a correlation coefficient of 0.91 and an RMSE of approximately 28.6 hPa compared with DEM-derived pressure. The reconstructed elevation further shows a correlation coefficient of approximately 0.93 and an RMSE of about 0.23 km compared with DEM， effectively reflecting regional topographic variations. Sensitivity analysis indicates that aerosol optical thickness in the range of 0.4-2.0 has a limited impact on the retrieval error， generally within about 1%， whereas elevation perturbations exert a more significant influence. Under ±10% elevation perturbations， the relative pressure error ranges from approximately 1% to 8%. Overall， this study verifies the feasibility of retrieving cloud-top pressure and reconstructing surface elevation based on satellite polarization imaging data， and provides a basis for future three-dimensional reconstruction schemes under integrated observations with high spatial， spectral， and polarization resolution.  
    关键词：multi-angle polarimetric instrument;cloud-top pressure;oxygen A absorption band;differential absorption spectroscopy   
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    ZHANG Rui, KONG Quanhuizi, WU Zhixu, XUE Peng, WANG Zhibin, JING Ning, TAO Juntong

    DOI：10.37188/OPE.20263411.1652
    摘要：In full-Stokes polarization imaging based on liquid crystal modulation and rotating waveplate methods， the influence of phase retardation errors arising from the neglect of oblique incidence in polarization elements has not been adequately considered， leading to reduced measurement accuracy. Building upon conventional polarization measurement approaches， error correction models for waveplate and liquid crystal phase retardation were established as functions of the beam incidence angle. The quantitative relationships between waveplate retardation and both incidence angle and wavelength， as well as between liquid crystal retardation and incidence angle， driving voltage， and wavelength， were systematically characterized. A 1∶1 secondary imaging telecentric relay optical system was designed to enable flexible switching among standard commercial lenses. Experimental results demonstrate that， with the incorporation of incidence-angle correction， the maximum relative errors in the degree of polarization， linear polarization degree， circular polarization degree， and ellipticity angle were reduced by approximately 7.72%， 8.31%， 10.50%， and 12.93%， respectively. These findings provide a theoretical foundation and technical support for high-precision polarization imaging， as well as for the optimized design and engineering application of polarization optical systems under oblique incidence conditions.  
    关键词：polarization imaging;liquid crystal modulation;rotating waveplate method;full-stokes;error correction   
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    CHEN Bin, LIU Haizheng, SHI Zelin, TONG Qiunan

    DOI：10.37188/OPE.20263411.1665
    摘要：To address the surface normal ambiguity in Shape from infrared polarization and the lack of physical constraints in purely data-driven methods， a reconstruction approach integrating infrared polarization physical priors with deep learning is proposed to improve the accuracy and stability of surface normal estimation and achieve high-precision 3D reconstruction. First， polarization information， including infrared intensity， degree of linear polarization （DoLP）， and angle of polarization （AoP）， is extracted from raw polarization images. Based on this， the zenith angle of the surface normal is recovered according to the relationship between the zenith angle and DoLP derived from a mixed polarization radiation model. Meanwhile， two candidate azimuth angles are computed using the correspondence between AoP and surface geometry， thereby constructing candidate surface normals. In parallel， a ThermalUNet is designed to generate reference surface normals from polarization information. The reference normals are further employed to constrain and refine the candidate normals derived from the physical model， resulting in a consistent and stable normal field. Experimental results on the public ThermoPol16 long-wave infrared polarization dataset demonstrate that the proposed method achieves a mean angular error of 8.36°， with 81.97% of pixels having angular errors less than 11.25°， outperforming existing methods. Furthermore， validation on a self-collected mid-wave infrared polarization dataset shows that the Dataset-level mean angular error is 7.3° for spherical targets with different temperatures and materials， indicating that the proposed method can stably and accurately recover surface geometry. In summary， the proposed method effectively resolves the normal ambiguity in polarization-based 3D reconstruction and exhibits strong accuracy and robustness under varying materials， temperatures， and infrared spectral bands.  
    关键词：infrared polarization imaging;shape from polarization;computational imaging;surface normal vector   
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    更新时间：2026-06-24

    LI Xiao, SUN Zhongqiu, LU Shan

    DOI：10.37188/OPE.20263411.1684
    摘要：A method for retrieving leaf surface roughness based on a multi-angular polarized reflectance model is proposed. By incorporating a non-polarized component into the Litvinov polarized reflectance model， an improved model was developed to establish a quantitative relationship between the leaf reflectance factor and surface roughness. Nineteen leaf samples from five plant species with distinct surface structures were used， and multi-angular leaf measurements in the principal plane were conducted to systematically evaluate the model performance in reflectance simulation and roughness inversion. The results showed that the improved model accurately reproduced the leaf reflectance factor across the 350-2 500 nm spectral range， with high overall accuracy （R²=0.99） and strong stability under varying viewing zenith angles （relative root mean square error <5%）. For roughness retrieval， robust performance was achieved across most wavelengths （R² generally >0.5）， with the highest accuracy observed at 450 and 550 nm （R²>0.7）. Differences in leaf surface structure were effectively distinguished， and relative variations in roughness were reliably captured. Sensitivity analysis indicated that leaf surface roughness and illumination-viewing geometry jointly controlled the magnitude and angular distribution of the polarized reflectance factor. Specifically， roughness was found to govern the magnitude and angular distribution of the leaf reflectance factor in the principal plane. This study demonstrates the feasibility of retrieving leaf surface roughness using a polarized reflectance-based model. The proposed approach overcomes the limitations of conventional intensity-based methods in characterizing surface structural parameters and provides a new pathway for vegetation structure retrieval， with potential extension to canopy-scale applications.  
    关键词：polarized reflectance model;leaf surface roughness;multi-angular observation;leaf parameter inversion   
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    更新时间：2026-06-24
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