|
|
|
|
|
|
| An Error Analysis Method for Spatial Heterodyne Interferograms Using Wavelet Transformation |
| WANG Fang-yuan1, 2, LI Xiao-jing1, 2, YE Song1, 2, LI Shu1, 2, WANG Xin-qiang1, 2* |
1. School of Optoelectronic Engineering, Guilin University of Electronic Technology, Guilin 541004，China
2. Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin 541004，China
|
|
|
|
|
Abstract Spatial Heterodyne Spectroscopy (SHS) is a hyperspectral analysis technique applied in weak matter detection, planetary exploration, and atmospheric remote sensing. Influenced by the complex experimental environment and the weak signal of the measured target, the measurement results of the Spatial Heterodyne Spectrograph are prone to tilted interference fringes and speckle noise in the interferograms, which ultimately affect the accuracy of the measured spectra. To solve these problems, this paper proposes a wavelet transform-based interferogram error analysis method, which effectively evaluates the interferogram's speckle noise level and stripe tilt error, thus providing a theoretical basis for the subsequent error correction. In this paper, Gaussian noise with different standard deviations is added to the error-free simulated interferograms of potassium lamps. The complex wavelet coefficients are extracted by using wavelet decomposition. Three noise estimation methods are used to compute the interferogram data. The results show that the noise estimation results of the spatial correlation wavelet transform method have the smallest deviation from the real value. The Donoho estimation method matches the real value better after the intercept correction. Subsequently, the Db2 wavelet decomposes the potassium lamp interferograms with different noise levels in one wavelet layer and extracts the detail coefficients in the horizontal, diagonal, and vertical directions. Donoho estimation is used to analyze the interferograms for two-dimensional noise analysis, which effectively characterizes the noise levels of the three interferograms in each direction. The research results show that the three directions of the wavelet detail coefficient valuation change respectively in the decomposition to different layers have a similar rule of change: the wavelet detail coefficient valuation and the stripe tilt direction, and the wavelet detail coefficient belongs to the direction of the angle is inversely proportional to the direction of the stripe tilt direction and the wavelet detail coefficient of the direction of the smaller the angle of the stripe tilt direction and the direction of the wavelet detail coefficient of the valuation of the larger, and vice versa, the smaller, and the horizontal direction of the first layer of the decomposition, the diagonal decomposition of the direction to the second layer of the vertical direction of the third layer of the hierarchy conforms to the rule of change. This study is a useful attempt to utilize wavelet decomposition for spatial outlier spectral error analysis and also provides a new way for subsequent correction of interferogram streak tilt error.
|
|
Received: 2023-10-13
Accepted: 2024-04-08
|
|
|
|
Corresponding Authors:
WANG Xin-qiang
E-mail: xqwang2006@126.com
|
|
[1] Egan M J, Acosta-Maeda T E, Angel S M, et al. Journal of Raman Spectroscopy, 2020, 51(9): 1794.
[2] Gojani A B, Palásti D J, Paul A, et al. Applied Spectroscopy, 2019, 73(12): 1409.
[3] Qiu J, Qi X, Li X, et al. Journal of Raman Spectroscopy, 2019, 50(10): 1602.
[4] Ciaffoni L, Matousek P, Parker W, et al. Applied Spectroscopy, 2021, 75(3): 241.
[5] Zhang Wenli, Liu Zhaoyu, Liang Kun, et al. Scientific Reports, 2022, 12(1): 1423.
[6] ZHANG Ming, PENG Wen, LAI Zhen-quan, et al(张 铭, 彭 文, 赖珍荃, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 797.
[7] Grishina S, Kodera P, Goryainov S, et al. Journal of Raman Spectroscopy, 2020, 51(12): 2505.
[8] Egan M J, Angel S M, Sharma S K. Applied Spectroscopy, 2018, 72(6): 933.
[9] Strange K A, Paul K C, Angel S M. Applied Spectroscopy, 2017, 71(2): 250.
[10] Wang J, Liu K, Jin S, et al. Applied Spectroscopy, 2020, 74(2): 130.
[11] Hu G, Xiong W, Shi H, et al. Journal of Raman Spectroscopy, 2016, 47(3): 289.
[12] Chi M, Han X, Xu Y, et al. Applied Spectroscopy, 2019, 73(1): 78.
[13] Kandjani A E, Griffin M J, Ramanathan R, et al. Journal of Raman Spectroscopy, 2013, 44(4): 608.
[14] Qiu J, Li X T, Qi X D. IEEE Photonics Journal, 2019, 11(5): 6802612.
[15] Xi Y, Li Y, Duan Z, et al. Applied Spectroscopy, 2018, 72(12): 1752.
[16] Zhang Zhen, Ye Yicheng, Luo Binyu, et al. Scientific Reports, 2022, 12(1): 22186.
[17] Chen H, Xu W, Broderick N, et al. Journal of Raman Spectroscopy, 2018, 49(9): 1529.
[18] Song Yue, Li Houpu, Zhai Guojun, et al. Scientific Reports, 2021, 11(1): 16988.
[19] Yang Xinlu, Wang Wenbo. Scientific Reports, 2023, 13: 18873.
[20] Zhou Xiaohui. Scientific Reports, 2023, 13(1): 9532.
[21] Ganjalizadeh V, Meena G G, Wall T A, et al. Nature Communications, 2022, 13(1): 1035.
[22] Jiang Shiyi, Xu Yang, Li Danyang, et al. Scientific Reports, 2022, 12(1): 20305.
[23] Soto M A, Ramírez J A, Thévenaz L. Nature Communications, 2016, 7(1): 10870.
[24] Peng Ziran, Wang Guojun. Scientific Reports, 2017, 7(1): 4564.
|
| [1] |
FAN Jie-jie1, 2, QIU Chun-xia1, FAN Yi-guang2, CHEN Ri-qiang2, LIU Yang2, BIAN Ming-bo2, MA Yan-peng2, YANG Fu-qin4, FENG Hai-kuan2, 3*. Wheat Yield Prediction Based on Continuous Wavelet Transform and
Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2890-2899. |
| [2] |
SUN Jia-qi, YIN Yong*, YU Hui-chun, YUAN Yun-xia, GUO Lin-ge. Hyperspectral Prediction Method for Perilla Aldehyde Content in Perilla Frutescens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2667-2674. |
| [3] |
WANG Xin-qiang1, 3, WANG Zhen1, 3, LIANG Qiu-yu1, 3, XIONG Wei2, 4, LI Zhi-wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, WANG Fang-yuan1, 3*. Research on Spectral Interferogram Bidirectional Correction Method for Spatial Heterodyne Remote Sensing Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 963-968. |
| [4] |
LI He1, WANG Yu2, FAN Kai2, MAO Yi-lin2, DING Shi-bo3, SONG Da-peng3, WANG Meng-qi3, DING Zhao-tang1*. Evaluation of Freezing Injury Degree of Tea Plant Based on Deep
Learning, Wavelet Transform and Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 234-240. |
| [5] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [6] |
DU Zhi-heng1, 2, 3, HE Jian-feng1, 2, 3*, LI Wei-dong1, 2, 3, WANG Xue-yuan1, 2, 3, YE Zhi-xiang1, 2, 3, WANG Wen1, 2, 3. A New EDXRF Spectral Decomposition Method for Sharpening Error Wavelets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1719-1724. |
| [7] |
LI Wei1, 2, HE Yao1, 2, LIN Dong-yue2, DONG Rong-lu2*, YANG Liang-bao2*. Remove Background Peak of Substrate From SERS Signals of Hair Based on Gaussian Mixture Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 854-860. |
| [8] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
| [9] |
WANG Fan1, 2, CHEN Long-yue2, 3, DUAN Dan-dan1, 2, 4*, CAO Qiong1, 4, ZHAO Yu1, LAN Wan-rong5. Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on
Wavelet Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3235-3242. |
| [10] |
YU Xin, ZHOU Wei*, XIE Dong-cai, XIAO Feng, LI Xin-yu. The Study of Digital Baseline Estimation in CVAFS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2392-2396. |
| [11] |
JIANG Rong-chang1, 2, GU Ming-sheng2, ZHAO Qing-he1, LI Xin-ran1, SHEN Jing-xin1, 3, SU Zhong-bin1*. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1385-1392. |
| [12] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [13] |
Yumiti Maiming1, WANG Xue-mei1, 2*. Hyperspectral Estimation of Soil Organic Matter Content Based on Continuous Wavelet Transformation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1278-1284. |
| [14] |
WANG Xin-qiang1, 3, HU Feng1, 3, XIONG Wei2, YE Song1, 3, LI Shu1, 3, GAN Yong-ying1, 3, YIN Shan1, 3, WANG Fang-yuan1, 3*. Research on Raman Signal Processing Method Based on Spatial Heterodyne[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 93-98. |
| [15] |
CHEN Feng-nong1, SANG Jia-mao1, YAO Rui1, SUN Hong-wei1, ZHANG Yao1, ZHANG Jing-cheng1, HUANG Yun2, XU Jun-feng3. Rapid Nondestructive Detection and Spectral Characteristics Analysis of Factors Affecting the Quality of Dendrobium Officinale[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3276-3280. |
|
|
|
|
