發展立方衛星搭載高光譜儀資料標準流程與校正實驗

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沈威安(Michael W. Shen) 查詢紙本館藏

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太空科學研究所

論文名稱

發展立方衛星搭載高光譜儀資料標準流程與校正實驗
(Data Processing and Calibration for a Hyperspectral Imager Onboard a CubeSat)

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★ 立方衛星高光譜儀成像系統開發以及校正	★ 高光譜儀的光機構分析與模擬

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至系統瀏覽論文 (2027-8-1以後開放)

摘要(中)

立方衛星體積小、重量輕、發射載具的成本低，使其發射需求逐年增加。鑒於立方衛星高靈活度的特性，適用於各種任務目標；我們開發了一個空間尺寸4U、重量1.4 kg的高光譜科學酬載，命名為HyperSCAN (Hyper SpecCral AnNlyzer)。此高光譜儀搭載在12U的鯨鯊號立方衛星，觀測的方式以推掃式進行地面資訊的線性掃描；將可見光以及近紅外光譜細分成162個波段，解析地面資料中的光譜特徵，提供地形地物分類以及輻射亮度量測的研究。高光譜儀搭載高感度CMOS感測器。此小型高光譜儀提供具有科學價值且低成本的影像資料，為未來搭載在衛星上的高光譜開發，進行先期的小型衛星測試實驗。
本論文工作開發具有科學價值資料，包含資料標準流程設計、科學化校正以及資料驗證三個方面。資料標準流程設計方面，分成3個資料層級，分別是Level 0衛星原始封包資料、Level 1高光譜立方資料 (包含空間以及光譜維度) 以及Level 2絕對輻射校正資料。資料流程設計可以幫助即時做後端的資料處理，提供地面資料標準化作業。高光譜資料校正方面，包含視野校正、波長校正以及絕對輻射校正。首先使用焦距無限遠的平行光進行視野校正，利用精密六軸進行旋轉角調整，測得視野寬度約 8.03度，對應500公里軌道，地面的像幅寬度70.2公里，沿軌長度33.7公里。第二，利用單光儀進行波長校正，測出波長範圍在可見-近紅外光波段430 - 800 nm，光譜解析度 < 10 nm，以550 nm的單色光來說 FWHM為8.72 nm。第三，感測器光訊號的輻射解析度為10 bits。因此，我們使用單光儀和積分球等光學儀器進行絕對輻射校正。其目的是將讀取到的DN值轉化成光譜輻射亮度 (watt/cm2-μm-sr)，感測器感測範圍會落在10-5~10-2 watt/cm2-μm-sr，符合高光譜所測量的大氣層頂輻射亮度範圍 (10-3~10-2 watt/cm2-μm-sr)。在資料訊噪比 (Signal-to-Noise Ratio, SNR) 的表現上，以單光儀資料計算SNR都有 > 15 dB。資料驗證方面，我們進行實際戶外側掃觀測，利用上述的資料流程以及資料校正後，我們與標準的輻射傳輸模型 (MODTRAN) 來做比較，量測的輻射亮度數量級為10-3~10-2 watt/cm2-μm-sr與MODTRAN所估計的大氣背景輻射亮度在同一個量級範圍。
另外我們分析目標物的絕對輻射亮度光譜特徵，包含草地、建築物、玻璃以及背景大氣。此外，也使用主成分分析進行資料降維，根據波段的數量得到162個特徵。依權重進行排序之後，以線性 SVM 進行高光譜資料的分類，若輸入有162個特徵的原始Level 2資料：分類準確度高達99%；當使用主成分分析降維至10個特徵：分類準確度一樣能維持99% 且訓練時間也降低。因此，適當的降維資料能減少高光譜儀資料的冗餘以及擷取光譜特徵，得到良好的分類效果。
本論文發展科學價值高光譜資料，所量測的輻射亮度符合在軌的需求，且經由實際觀測驗證，證明高光譜儀量測光譜特徵以及輻射亮度，能進行地面資料分類的應用，驗證其未來發展小型衛星高光譜儀的研究價值。

摘要(英)

The small size and light weight of CubeSats, as well as the low cost of the launch vehicle, has driven an increase in progress in CubeSat missions. Due to the high flexibility of CubeSats, which are suitable for various mission objectives, we have developed a 4U space size, 1.4kg hyperspectral scientific payload named HyperSCAN (Hyper SpecCral AnNlyzer). The HyperSCAN is onboard the 12U SCintillation and IONsophere eXtended (SCION-X) CubeSat, using the pushbroom method to scan ground information linearly in sweeping mode. The visible and near-infrared spectra are subdivided into 162 bands, and the spectral features in the ground data are analyzed, which can be used to provide research on the classification of terrain and features as well as the measurement of absolute radiance. With a high-sensitivity CMOS sensor, HyperSCAN offers scientifically valuable and low-cost image data for the scientific community and develops the future hyperspectral imager onboard small satellite missions.
The aims of this thesis consist of three aspects: standard data processing flow, scientific calibration, and data validation. The data flow design is divided into three levels: Level 0 raw packet data, Level 1 hyperspectral cubic dataset (including two spatial and one spectral dimension), and Level 2 absolute radiometric calibration data. The data flow design can help the back-end data processing in real-time and provide ground data standardization.
The calibration process for HyperSCAN is a meticulous one, ensuring the accuracy and reliability of the data it provides. This includes field-of-view (FOV) correction, wavelength measurement, and absolute radiometric calibration. For FOV correction, we use parallel light with infinite focal length to carry out FOV experiment, and use precision six-axis to carry out rotation angle adjustment to measure the FOV of ~ 8.0 degrees, corresponding to the image width on the ground is 70.2 km at the 500 km orbit, and along-track length of 33.7 km. The wavelength correction was carried out by using a monochromator, and the wavelength was measured between 430 - 800 nm in the visible-near-infrared band. The FWHM was 8.72 nm for monochromatic light of 550 nm. We calibrated absolute radiometric calibration using a power meter and an integrating sphere for flat field measurement, and converted the readout 10-bit digital number (DN) value into the absolute spectral radiance (watt/cm2-μm-sr). The HyperSCAN ranges from 10-5 to 10-2 watt/cm2-μm-sr, within the typical range of the top-of-atmosphere radiance (10-3~10-2 watt/cm2-μm-sr). The SNR of HyperSCAN is > 15 dB.
We conducted an outdoor experiment for data validation and calibrated absolute spectral radiance using the above data processing flow. Compared with the standard radiation transmission model (MODTRAN), the measured radiance quantities range within the order of magnitude of 10-3~10-2 watt/cm2-μm-sr, closely matching the model results. Finally, we used the principal component analysis (PCA) to reduce the data dimensions where original data have 162 bands. After sorting the weights of the dataset, the linear Support Vector Machine was used to categorize the hyperspectral data. The classification accuracy was as high as 99% for a total of 162 bands. After PCA, if we downscaled the required spectral features to 10 dominant features, the classification accuracy was as high as 99% and required less training time, further demonstrating the reliability of our results. Therefore, the classification results demonstrate that the advantages of PCA, include data dimensionality reduction and spectral feature extraction.
This thesis develops HyperSCAN’s calibrations and data validation experiment, conforming to in-orbit requirements. We verified that the HyperSCAN performance could be applied to the ground data classification and satisfy the criteria of the future development of a small-satellite hyperspectral imager payload.

關鍵字(中)

★ 高光譜儀
★ 高光譜影像
★ 絕對輻射校正
★ 調製轉換函數
★ 主成分分析

關鍵字(英)

★ Hyperspectral Imager
★ Hyperspectral Imaging
★ Absolute Radiometric Calibration
★ Modulation Transfer Function
★ Principal Component Analysis

論文目次

摘要 i
Abstract iii
誌謝 vi
目錄 vii
圖目錄 xi
表目錄 xv
一、緒論 1
1-1 前言 1
1-2 觀測波段與目的 3
1-3 論文概要 4
二、研究原理與資料處理方法 5
2-1 高光譜儀成像原理 5
2-1-1 掃描方式 6
2-1-2 光學系統元件 8
2-2 資料標準流程設計 14
2-2-1 資料層級說明 (Level 0 - Level 2) 14
2-2-2 資料格式與命名方式 15
2-2-3 設計流程呈現 15
2-3 計算資料的相關參數 16
2-3-1 繞行軌道參數 16
2-3-2 掃描寬度 (Swath Width) 17
2-3-3 瞬間視場 (Instantaneous Field Of View , IFOV) 18
2-3-4 地面解析度單元 (Ground Resolution Cell, GRC) 18
2-3-5 地面採樣距離 (Ground Sampling Distance, GSD) 19
2-3-6 沿軌長度 (Along-track Length) 21
2-3-7 空間維度比例調整 23
2-3-8 曝光時間測試 23
三、實驗方法與結果 25
3-1 校正儀器介紹 25
3-1-1 準直儀 (Collimator) 25
3-1-2 六軸聯動定位系統 (Hexapods) 26
3-1-3 單光儀 (Monochromator) 27
3-1-4 積分球 (Integrating Sphere) 28
3-1-5 光功率計 (Power Meter) 28
3-2 視野校正 30
3-2-1 實驗目的 30
3-2-2 實驗流程 30
3-2-3 實驗結果 31
3-3 波長校正 33
3-3-1 實驗目的 33
3-3-2 實驗流程 33
3-3-3 實驗結果 34
3-4 絕對輻射校正 34
3-4-1 實驗目的 34
3-4-2 實驗流程 35
3-4-3 光功率計量測結果 37
3-4-4 高光譜儀量測結果 39
3-4-5 FWHM計算結果 41
3-4-6 光譜輻射亮度結果 46
3-4-7 訊噪比 (SNR) 51
3-4-8 輻射線性度檢驗 54
3-4-9 MTF量測 55
四、觀測結果與分析 60
4-1 實際側掃資料 60
4-2 MODTRAN 模擬驗證 62
4-3 高光譜影像分類 64
4-3-1 PCA降維資料 65
4-3-2 機器學習分類結果 67
五、結論 71
5-1 論文工作 71
5-2 未來展望 72
個人經歷簡介 73
參考文獻 75
附錄一、HyperSCAN統整規格表 81
附錄二、HYSPO-1立方衛星統整規格表 82
附錄三、積分球帶通濾光片規格 83
附錄四、減光片規格表 85

參考文獻

[1] J. Alicia, " CubeSat Design Specification Rev. 14.1," Cal Poly SLO., 2020.
[2] K. Woellert, P. Ehrenfreund, A. J. Ricco, and H. Hertzfeld, "Cubesats: Cost-effective science and technology platforms for emerging and developing nations," Advances in Space Research, vol. 47, no. 4, pp. 663-684, 02/15 2011, doi: https://doi.org/10.1016/j.asr.2010.10.009.
[3] E. Kulu, Nanosatellite Launch Forecasts 2022 - Track Record and Latest Prediction. 2022.
[4] S. E. Qian, "Hyperspectral Satellites, Evolution, and Development History," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 7032-7056, 2021, doi: https://doi.org/10.1109/JSTARS.2021.3090256.
[5] A. Goetz, "Imaging spectrometry for remote sensing: vision to reality in 15 years," Proc. SPIE, vol. 2480, pp. 1-13, 1995, doi: https://doi.org/10.1117/12.210867.
[6] M. A. Folkman, J. Pearlman, L. B. Liao, and P. J. Jarecke, "EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration," Hyperspectral Remote Sensing of the Land and Atmosphere, vol. 4151, pp. 40-51, 2001, doi: https://doi.org/10.1117/12.417022.
[7] J. Pearlman, S. Carman, C. Segal, P. Jarecke, P. Clancy, and W. Browne, "Overview of the Hyperion Imaging Spectrometer for the NASA EO-1 mission," in IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), 09-13 July 2001, vol. 7, pp. 3036-3038, doi: https://10.1109/IGARSS.2001.978246.
[8] M. Bigas, E. Cabruja, J. Forest, and J. Salvi, "Review of CMOS image sensors," Microelectronics journal, vol. 37, no. 5, pp. 433-451, 2006, doi: https://doi.org/10.1016/j.mejo.2005.07.002.
[9] J. Praks et al., "Miniature Spectral Imager in-Orbit Demonstration Results from Aalto-1 Nanosatellite Mission," in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 22-27 July 2018 2018, pp. 1986-1989, doi: https://10.1109/IGARSS.2018.8517658.
[10] S. Conticello et al., Hyperspectral Imaging for real time land and vegetation inspection. 2016.
[11] S. Bakken et al., "HYPSO-1 CubeSat: First Images and In-Orbit Characterization," Remote Sensing, vol. 15, no. 3, p. 755, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/3/755.
[12] L. Guanter et al., "The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation," Remote Sensing, vol. 7, no. 7, pp. 8830-8857, 2015. [Online]. Available: https://www.mdpi.com/2072-4292/7/7/8830.
[13] J. Transon, R. D’Andrimont, A. Maugnard, and P. Defourny, "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context," Remote Sensing, vol. 10, no. 2, p. 157, 2018. [Online]. Available: https://www.mdpi.com/2072-4292/10/2/157.
[14] M. Govender, K. Chetty, and H. Bulcock, "A review of hyperspectral remote sensing and its application in vegetation and water resource studies," Water Sa, vol. 33, no. 2, pp. 145-151, 2007.
[15] J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, "Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA," Remote Sensing, vol. 6, no. 2, pp. 1007-1025, 2014.
[16] L. M. Kandpal, S. Lee, M. S. Kim, H. Bae, and B.-K. Cho, "Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels," Food Control, vol. 51, pp. 171-176, 2015/05/01/ 2015, doi: https://doi.org/10.1016/j.foodcont.2014.11.020.
[17] A. A. Gowen, C. P. O′Donnell, P. J. Cullen, G. Downey, and J. M. Frias, "Hyperspectral imaging – an emerging process analytical tool for food quality and safety control," Trends in Food Science & Technology, vol. 18, no. 12, pp. 590-598, 2007/12/01/ 2007, doi: https://doi.org/10.1016/j.tifs.2007.06.001.
[18] B. Fei, "Chapter 3.6 - Hyperspectral imaging in medical applications," in Data Handling in Science and Technology, vol. 32, J. M. Amigo Ed.: Elsevier, 2019, pp. 523-565.
[19] H. Liang, "Advances in multispectral and hyperspectral imaging for archaeology and art conservation," Applied Physics A, vol. 106, pp. 309-323, 2012.
[20] D. G. L. Manolakis, Ronald B./ Cooley, Thomas W., Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms. Cambridge University Press, 2016, pp. 1-706.
[21] S. Kaiser, B. Sang, J. Schubert, S. Hofer, and T. Stuffler, Compact prism spectrometer of pushbroom type for hyperspectral imaging (Optical Systems Design). SPIE, 2008.
[22] T.-H. Chao, H. Zhou, X. Xia, and S. Serati, "Hyperspectral imaging using electro-optic Fourier transform spectrometer," Proc. SPIE, vol. 5437, 4/12 2004, doi: https://doi.org/10.1117/12.548075.
[23] N. T. a. A. L. B. Geelen. "New multi- and hyperspectral cameras cover diverse applications." https://www.photonics.com/Articles/New_Multi-_and_Hyperspectral_Cameras_Cover/a53223 (accessed June, 2024).
[24] R. O. Green et al., "Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)," Remote Sensing of Environment, vol. 65, no. 3, pp. 227-248, 1998/09/01/ 1998, doi: https://doi.org/10.1016/S0034-4257(98)00064-9.
[25] M. B. Stuart, A. J. S. McGonigle, and J. R. Willmott, "Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems," Sensors, vol. 19, no. 14, p. 3071, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/14/3071.
[26] "實驗三光的干涉與繞射." 國立陽明交通大學. https://physlab.ep.nycu.edu.tw/wp-content/uploads/2016/11/110II_d03-%E5%85%89%E7%9A%84%E7%B9%9E%E5%B0%84%E8%88%87%E5%B9%B2%E6%B6%89.pdf (accessed June, 2024).
[27] "CP140 Series Spectrographs Datasheet." https://www.horiba.com/fileadmin/uploads/Scientific/Documents/OEM/cp140.pdf (accessed June, 2024).
[28] C. Palmer, DIFFRACTION GRATING HANDBOOK. Newport Corporation.
[29] "Mars Perseverance Rover Case Study PYTHON Image Sensors Add Vision to Mars Perseverance EDL." onsemi. https://www.onsemi.com/pub/collateral/nasa-perseverance-rover-case-study.pdf (accessed June, 2024).
[30] "CMOS Image Sensor Datasheet." https://www.onsemi.com/pdf/datasheet/noip1sn5000a-d.pdf (accessed June, 2024).
[31] Yu-Shun Wang et al., "SCintillation and IONosphere eXtended (SCION-X): A 12U CubeSat for Ionospheric and Atmospheric Science," in Small Satellite Conference Utah State University, Logan, UT, 2023. [Online]. Available: https://digitalcommons.usu.edu/smallsat/2023/all2023/241. [Online]. Available: https://digitalcommons.usu.edu/smallsat/2023/all2023/241
[32] "Collimator Datasheet." https://www.inframet.com/Data_sheets/CDT.pdf (accessed June, 2024).
[33] "HXP50 Hexapods Datasheet." https://www.newport.com/mam/celum/celum_assets/np/resources/DS-102101_HXP50_Datasheet.pdf?0 (accessed June, 2024).
[34] "Monochromator Datasheet." https://www.newport.com/medias/sys_master/images/images/h2c/hc0/9134874230814/DS-121402-Cornerstone-130.pdf (accessed June, 2024).
[35] "Xe Lamp Datasheet." https://www.newport.com/medias/sys_master/software/software/h8e/h99/9129427173406/LH300-LHC300-REV02-print.pdf (accessed June, 2024).
[36] "Intergrating Sphere Datasheet." https://www.newport.com/mam/celum/celum_assets/np/resources/DS-011109_Integrating_Sphere_Detectors.pdf?0 (accessed 06, 2024).
[37] "Halogen Lamp Specification." https://estore.ushio.com/products/1000382-evc-fgx-jc-250w (accessed June, 2024).
[38] "Optical Power Meter Datasheet." https://www.newport.com/medias/sys_master/images/images/h3b/hb5/8796989751326/1936-R-2936-R-Power-and-Energy-Meter-Datasheet.pdf (accessed 06, 2024).
[39] "Photodiode Sensors Datasheet." https://www.newport.com/medias/sys_master/images/images/hf0/hb9/8797036544030/918D-Series-High-Performance-Photodiode-Sensors-Data-Sheet.pdf (accessed June, 2024).
[40] "LM100JC1MS | 2/3" 100mm 2MP C-Mount Lens." https://www.kowa-lenses.com/media/pdf/2c/af/f5/LM100JC1MS_03_2020.pdf (accessed June, 2024).
[41] S. R. Walker JH, Hattenburg AT., NBS Measurement Services: Spectral Radiance Calibrations. Natl. Bur. Stand. (U.S.), Spec., 1987.
[42] 涂家豪, "立方衛星高光譜儀成像系統開發以及校正," 碩士, 太空科學與工程研究所, 國立中央大學, 桃園縣, 2020. [Online]. Available: https://hdl.handle.net/11296/pkd9tj
[43] "Thermal insulation — Heat transfer by radiation — Physical quantities and definitions," 1989. [Online]. Available: https://www.iso.org/standard/16943.html
[44] A. Boukhayma, A. Peizerat, and C. Enz, "Noise Reduction Techniques and Scaling Effects towards Photon Counting CMOS Image Sensors," Sensors, vol. 16, no. 4, p. 514, 2016. [Online]. Available: https://www.mdpi.com/1424-8220/16/4/514.
[45] Y. Cao, Z. He, J. Yang, X. Ye, and Y. Cao, "A multi-scale non-uniformity correction method based on wavelet decomposition and guided filtering for uncooled long wave infrared camera," Signal Processing: Image Communication, vol. 60, pp. 13-21, 2018/02/01/ 2018, doi: https://doi.org/10.1016/j.image.2017.08.013.
[46] "LH300 Spectral Output." https://www.newport.com/p/LH300 (accessed June, 2024).
[47] "Landsat 7 Science Data Users Handbook." https://landsat.gsfc.nasa.gov/wp-content/uploads/2016/08/Landsat7_Handbook.pdf (accessed June, 2024).
[48] J. A. Barsi, K. Lee, G. Kvaran, B. L. Markham, and J. A. Pedelty, "The Spectral Response of the Landsat-8 Operational Land Imager," Remote Sensing, vol. 6, no. 10, pp. 10232-10251, 2014. [Online]. Available: https://www.mdpi.com/2072-4292/6/10/10232.
[49] C. Dalitz, R. Pohle-Frohlich, and T. Michalk, "Point spread functions and deconvolution of ultrasonic images," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 62, no. 3, pp. 531-544, 2015, doi: 10.1109/TUFFC.2014.006717.
[50] S. Zhang, F. Wang, X. Wu, and K. Gao, "MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments," Sensors, vol. 23, no. 1, p. 509, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/1/509.
[51] "X-RSW-E Series Datasheet." https://www.zaber.com/api/assets/X-RSW-E-Datasheet.pdf (accessed June, 2024).
[52] A. Berk et al., "MODTRAN 5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options: update," presented at the Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XI, 2005.
[53] "NOAA Solar Calculator." (accessed June, 2024).
[54] C. Rodarmel and J. Shan, "Principal component analysis for hyperspectral image classification," Surveying and Land Information Science, vol. 62, no. 2, pp. 115-122, 2002.
[55] J. Zabalza, J. Ren, J. Ren, Z. Liu, and S. Marshall, "Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging," Appl. Opt., vol. 53, no. 20, pp. 4440-4449, 2014/07/10 2014, doi: 10.1364/AO.53.004440.
[56] G. Liu, S. Mao, and J. H. Kim, "A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis," Sensors, vol. 19, no. 9, p. 2023, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/9/2023.
[57] "Cary 60 UV/VIS分光光度計規格表官網網站." https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-systems/cary-60-uv-vis-spectrophotometer#features (accessed June, 2024).

指導教授

郭政靈(Cheng-Ling Kuo)

審核日期

2024-8-14

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