4.7 Article

Calculation and restoration of lost spatial information in division-of-focal-plane polarization remote sensing using polarization super-resolution technology

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ELSEVIER
DOI: 10.1016/j.jag.2022.103155

Keywords

Polarization remote sensing; Super-resolution; Calibration method; Effect evaluation; Division -of -focal -plane

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This study proposes a new polarization super-resolution (PSR) remote sensor and a data recovery method to address the spatial resolution loss and information errors in division-of-focalplane (DoFP) polarization remote sensing systems. The experimental results demonstrate that the system can eliminate the spatial resolution loss caused by the DoFP technology and double the real image resolution in both the horizontal and vertical directions.
Spatial resolution plays a crucial role in the process of polarization remote sensing method for Earth observation, and the problem of resolution improvement has always been an important research direction in the field of remote sensing. To address the spatial resolution loss and information errors in division-of-focalplane (DoFP) polarization remote sensing systems, this study proposes a new polarization super-resolution (PSR) remote sensor and a data recovery method. We calibrate the relative displacement between image plane and detector in the laboratory, and verify the effectiveness of this method by actual external imaging. The experimental results demonstrate that the system can eliminate the spatial resolution loss caused by the DoFP technology, and the real image resolution is doubled in both the horizontal and vertical directions. We also verify the effectiveness of this new instrument and data recovery method. By comparing the results of this method with the existing algorithms, it is found that it has a great improvement under the same evaluation parameters, and the texture features of the target scene were significantly enhanced. Moreover, the system can simultaneously perceive multidimensional information, such as high-resolution intensity images and pixellevel polarization information of the target scene, and therefore, can potentially be applied in remote sensing systems.

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