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| Research on the Correction of Spatial Heterodyne Interference Data Based on Principal Component Analysis |
| WANG Xin-qiang1, 3, WANG Zhen1, 3, QIN Shan1, 3, XIONG Wei2, 4, WANG Fang-yuan1, 3, YE Song1, 3, NIE Kun1, 3* |
1. School of Optoelectronic Engineering, Guilin University of Electronic Technology, Guilin 541004, China
2. Hefei Institutes of Physical Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
3. Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin 541004, China
4. Key Laboratory of General Optical Calibration and Characterization of Chinese Academy of Sciences, Hefei 230031, China
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Abstract Spatial Heterodyne Spectroscopy（SHS）is a new hyperspectral remote sensing detection technology widely used in atmospheric observation, astronomical remote sensing, material identification, and other fields. Two-dimensional measured interferometric data acquired by SHS can be interfered with by various influences, of which high-frequency noise, irregular dark spots, and interferogram nonuniformity are among the most common. These effects reduce the accuracy of the recovered spectra, and therefore, effective data correction methods need to be developed for these effects to improve the accuracy of the inverted spectra. In this paper, two light sources, potassium and xenon lamps, are used to generate quasi-monochromatic and continuous light signals, and the interference data formed by them are used as the object of study. A spatial heterodyne interferogram data correction method based on principal component analysis is proposed to address the effects of multiple noises in these two measured interferograms. Firstly, the first-order difference method is used to preprocess all the row data of the measured interferograms to remove the baseline effects, and Fourier transforms the processed row data to obtain the spectral data. Then, all the line spectral data are subjected to principal component analysis, multiple mutually orthogonal principal components and the contribution of each principal component is calculated, and the principal components with a contribution of less than 2% are treated as noise and deducted. In contrast, the other principal components are retained as valid spectral signals for spectral reconstruction, and the reconstructed spectra are inverse Fourier transformed to obtain a corrected interferogram. Finally, the effectiveness of the calibration methods is comparatively analyzed in terms of interferogram and spectral dimensions. The results show that the dark spots in the measured interferograms of monochromatic and continuous two light sources are effectively deducted, and the effect of non-uniformity is greatly improved. The effects before and after spectral correction are compared for the data in rows 536, 600, and 982 of the interferogram, which are affected by the dark spots. The results show that the correction method effectively suppresses the high-frequency noise in the spectra and makes the spectra smooth and clear, and the details of the characteristic peaks and so on are highlighted. The signal-to-noise ratio is improved, and the mean square error of the three rows of spectra decreased from 0.037 77, 0.027 33, and 0.030 99 before correction to 0.013 31, 0.012 20, and 0.012 34 after correction, respectively, which quantitatively illustrates the effectiveness of the method.
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Received: 2023-10-25
Accepted: 2024-03-02
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Corresponding Authors:
NIE Kun
E-mail: 1079471255@qq.com
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[1] QIU Jun, CHEN Jin, PENG Rong-chao, et al(邱 俊, 陈 晋, 彭荣超, 等). The Journal of Light Scattering(光散射学报), 2022, 34(1): 36.
[2] PENG Xiang, LIU En-hai, TIAN Shu-lin, et al(彭 翔, 刘恩海, 田书林, 等). Acta Physica Sinica(物理学报), 2022, 71(24): 240601.
[3] Tarumi T, Small G W, Combs R J, et al. Vibrational Spectroscopy, 2005, 37(1): 39.
[4] YE Song, LI Yuan-zhuang, SUN Yong-feng，et al(叶 松, 李源壮, 孙永丰, 等). Infrared and Laser Engineering(红外与激光工程), 2018, 47(12): 1223001.
[5] CAO Qian(曹 前). Optical Technique(光学技术), 2023, 49(2): 250.
[6] WANG Cheng, JIAO Tong, LU Yu-fei, et al(王 成, 焦 彤, 陆雨菲, 等). Chinese Journal of Lasers(中国激光), 2020, 47(2): 0207030.
[7] LI Zhi-wei, SHI Hai-liang, LUO Hai-yan, et al(李志伟, 施海亮, 罗海燕, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(1): 29.
[8] WANG Run-hao, SUN Ying-ru, GAN Yin-lu, et al(王润昊, 孙影茹, 甘茵露, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2022, 59(19): 1930002.
[9] LUO Hai-yan, XIONG Wei, SHI Hai-liang, et al(罗海燕, 熊 伟, 施海亮, 等). Acta Optica Sinica(光学学报), 2017, 37(6): 0612001.
[10] Qiu J, Qi X D, Li X T, et al. Optics Express, 2018, 26(9): 11994.
[11] Wang Qiansheng, Luo Haiyan, Bai Yunfei, et al. Applied Optics，2023, 62(8): 2154.
[12] CAO Chi-peng, WANG Hui-qin, WANG Ke, et al(曹赤鹏, 王慧琴, 王 可, 等). Optics and Precision Engineering(光学精密工程), 2021, 29(10): 2444.
[13] LIU Shuang, YU Hai-ye, SUI Yuan-yuan, et al(刘 爽, 于海业, 隋媛媛, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2023, 43(5): 1550.
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