|
|
|
|
|
|
| Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View |
| YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3* |
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Computational Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China
|
|
|
|
|
Abstract Hyperspectral remote sensing can provide rich information on the earth's surface, so it has attracted extensive attention from scholars at home and abroad. Affected by the level of technology, a single hyperspectral imager cannot meet the application requirements of a large field of view and high resolution simultaneously. Splicing hyperspectral imager technology combines multiple hyperspectral imagers into one imaging system, effectively expands the field of view of hyperspectral imagers, and is widely used in precision agriculture, earth observation, environmental monitoring, etc. Due to the influence of detector pixel response, optical system, electronic system and other factors, the output of a single pixel of the detector will be inconsistent when the focal plane array of the hyperspectral imager is under the same uniform radiation source, which is the non-uniformity of the hyperspectral imager. The non-uniformity of splicing hyperspectral imager seriously affects the image quality and interpretation.Non-uniformity correction methods are divided into calibration-based and scene-based correction methods. In this paper, non-uniformity models of a single imager and multiple imagers are established based on the analysis of the non-uniformity of the splicing hyperspectral imager developed by Aerospace Information Research Institute. Based on non-uniformity models, a novel non-uniformity correction method based on overlapping fields of view is proposed. This method integrates the laboratory calibration and the real-time flight data. The laboratory radiometric calibration is used to correct non-uniformity of a single image, while the overlapping field of view and wavelet filter isused to correct non-uniformity of multiple imagers. This method only needs to calibrate the radiation of a single imager in the laboratory so that it eliminates the limitation of requiring a large-aperture integrating sphere. Several experiments are carried out to evaluate the quality of images processed by different methods. Images with non-uniformity of two different bands are selected as original images, and original images are processed by the proposed method. In order to quantitatively compare the correction effects of different methods, three evaluation indexes,Improvement Factor (IF), Non-uniformity (NU) and Spectral Angle (SA), are introduced. The results show that the proposed method can correct non-uniformity effectively and preserve the spectrum features simultaneously as much as possible.
|
|
Received: 2022-06-18
Accepted: 2022-09-21
|
|
|
|
Corresponding Authors:
NIE Bo-yang
E-mail: nieby@aircas.ac.cn
|
|
[1] TONG Qing-xi(童庆禧). Hyperspectral Remote Sensing(高光谱遥感). Beijing: Higher Education Press(北京：高等教育出版社), 2006.
[2] YAO Tao, YIN Shi-min, XIANGLI Bin, et al(姚 涛, 殷世民, 相里斌, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010，30(6): 5.
[3] Zhang L, Huang C, Wu T, et al. Sensors, 2011, 11(3): 2408.
[4] Bindschadler R, Choi H. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1189.
[5] Kumar R, Bhowmick S A, Babu K N, et al. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2268.
[6] Duan Y, Chen W, Wang M, et al. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5164.
[7] Horn B K P, Woodham R J. Computer Graphics & Image Processing, 1979, 10(1): 69.
[8] Wegener M. International Journal of Remote Sensing, 1990, 11(5): 859.
[9] Cao B, Du Y, Xu D, et al. Optik-International Journal for Light and Electron Optics, 2015, 126(23): 4723.
[10] Gadallah F L, Csillag F, Smith E J M. International Journal of Remote Sensing, 2000, 21(12): 2505.
[11] LI Xian-sheng, LIU Hong-xing, SUN Jing-xu，et al(李宪圣, 刘洪兴, 孙景旭, 等). Acta Optica Sinica(光学学报), 2017, 37(8): 10.
[12] Jia J, Wang Y, Zhuang X, et al. Infrared Physics & Technology, 2017, 81: 305.
|
| [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] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
JIANG Jun1, 2, YAO Zhi-gang1, 2*. Dynamic Detection and Correction for Abnormal Response of CCD Pixels in Spaceborne Low-Light Imager[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1175-1182. |
| [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. |
|
|
|
|
