|
|
|
|
|
|
| Fast Spectral Calibration Method of Spectral Imager |
| WANG Jian-wei1, 2, LI Wei-yan1, SUN Jian-ying1, LI Bing1, CHEN Xin-wen1, TAN Zheng1, ZHAO Na1, LIU Yang-yang1, 3, LÜ Qun-bo1, 3* |
1. The Key Laboratory of Computational Optic Imaging Technology, Academy of Opto-Electronics，Chinese Academy of Sciences, Beijing 100094，China
2. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
3. School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
|
|
|
|
|
Abstract Spectral calibration is determining the central wavelength of each channel of a spectrometer. To obtain the spectral radiance, it is usually necessary to calibrate the spectrometer and map the output value of the spectrometer to a physical quantity radiance. Different spectrometers, The spectral response is different, so it is necessary to determine the spectral response of each channel during the spectral calibration process. The spectral imager can be regarded as a composition of multiple spectrometers, and the center wavelength and spectral response of all points need to be calibrated. Since the birth of the first imaging spectrometer, its calibration method has been gradually fixed. A monochromator with the higher spectral resolution is required, and its spectral bandwidth is much smaller than the spectral response bandwidth of the spectral imager so that quasi-monochromatic light can be considered a pulse function. According to the characteristics of the pulse function, changing the wavelength of the quasi-monochromatic light and scanning the response wavelength range of the spectral imager is a process of sampling the spectral response function at intervals.. Therefore, the spectral imager’s central wavelength and spectral response function can be directly obtained from the spectral calibration data. With the development of technology, the sensitivity of the detector is getting higher and higher, and the resolution of the spectral imager is getting higher and higher. Higher requirements are put forward for the quasi-monochromatic light required for the spectrum calibration. However, the narrower the bandwidth of the quasi-monochromatic light, the lower its energy, and it takes longer to obtain data that meets the signal-to-noise ratio, which reduces the efficiency of calibration. In this paper, we combined the characteristics of quasi-monochromatic light’s spectral line type and spectral response function approximating to Gaussian function. Through theoretical analysis, a method of spectral calibration using wide-band quasi-monochromatic light is proposed, which can effectively reduce the calibration step of spectral calibration improves the efficiency of calibration and is suitable for the rapid calibration of spectral imagers. This method is used for the spectral calibration of a space-borne hyperspectral imager. The spectral imager uses a prism to split light and has the characteristics of non-linear dispersion. The spectral resolution varies from 2 to 18 nm, and there is a large curve of spectral lines. As a result, the center wavelength of each pixel is different, and spectral calibration is required for each pixel. To avoid the discontinuity of the central wavelength of the adjacent field of view caused by the calibration of the separate field of view, the quasi-monochromatic light spot emitted by the monochromator illuminates the entire slit, and a cylindrical lens and ground glass are placed between the slit and the monochromator. The cylindrical lens is used to converge the light perpendicular to the slit direction to improve the energy utilization; the ground glass is used to homogenize the light, and the presence of ground glass greatly reduces the energy entering the spectral imager. Combining the method proposed in this paper increases of the accuracy the bandwidth of monochromatic light, and the increase of energy have finally completed the rapid calibration of the spectral imager. The mercury lamp verifies that the spectral calibration accuracy is 0.23 nm.
|
|
Received: 2021-03-06
Accepted: 2022-01-09
|
|
|
|
Corresponding Authors:
LÜ Qun-bo
E-mail: lvqunbo@aircas.ac.cn
|
|
[1] Lu G，Fei B. Journal of Biomedical Optics, 2014, 19(1): 010901.
[2] Halicek M, Lu G, Little J, et al. Journal of Biomedical Optics, 2017, 22(6): 060503.
[3] Sahoo S K, Tang D Y，Dang C. Optica, 2017, 4(10): 1209.
[4] Khan M J, Khan H S, Yousaf A, et al. IEEE Access, 2018, 6: 14118.
[5] Bodner G, Nakhforoosh A, Arnold T, et al. Plant Methods, 2018, 14(1): 84.
[6] Zhang F, Chen C, Liu J, et al. Review of Scientific Instruments, 2018, 89(2): 023112.
[7] CHI Ming-bo, HAN Xin-xin, XU Yang, et al（迟明波, 韩欣欣, 徐 阳, 等）. Chinese Opics（中国光学）, 2020, 13(2): 249.
[8] XIE Chen-yu, ZHAI Wen-chao, LI Jian-jun, et al（谢臣瑜, 翟文超, 李健军，等）. Infrared and Laser Engineering（红外与激光工程）, 2020, 49(2): 0205005.
[9] Zhang H, Zheng Y, Lin C, et al. International Journal of Remote Sensing, 2017, 38(13): 3800.
[10] CUI Ji-cheng, ZHU Ji-wei, CUI Hong, et al（崔继承, 朱继伟, 崔 弘, 等）. Laser and Infrared（激光与红外）, 2019, 49(5): 584.
[11] Liu H, Zhang D, Wang Y. Sensors, 2019, 19(10): 2259.
|
| [1] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [2] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [3] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [4] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [5] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [6] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
| [7] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
| [8] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
| [9] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
| [10] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
| [11] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
| [12] |
ZHANG Fan1, WANG Wen-xiu1, ZHANG Yu-fan1, HU Ze-xuan1, ZHAO Dan-yang1, MA Qian-yun1, SHI Hai-yan2, SUN Jian-feng1*. Hyperspectral and Ensemble Learning Method for Rapid Identification of Black Spot in Yali Pear at Gley Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1541-1549. |
| [13] |
GUO Feng1, ZHAO Dong-e1*, YANG Xue-feng1, CHU Wen-bo2, ZHANG Bin1, ZHANG Da-shun3MENG Fan-jun3. Research on Hyperspectral Image Recognition of Iron Fragments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 997-1003. |
| [14] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
| [15] |
HE Lu1, WAN Li2, GAO Hui-yi2*. Recognition of Drought Stress in Tomato Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 724-730. |
|
|
|
|
