利用耦合非負矩陣分解方法進行Hyper-SCAN影像合成

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：115

、訪客IP：13.58.200.78

姓名

劉晏辰(Yen-Chen Liu) 查詢紙本館藏

畢業系所

太空科學與工程研究所

論文名稱

利用耦合非負矩陣分解方法進行Hyper-SCAN影像合成
(Hyper-SCAN image fusion using coupled non-negative matrix factorization)

相關論文

★ 科學酬載氣暉影像儀模擬設計	★ Analyzing the Sprite Emission Ratio from FORMOSAT-2 ISUAL Array Photometer Data
★ 太空規格影像感測器模組開發	★ 製作登陸小行星用的Scheimpflug相機系統
★ 聯合觀測喜馬拉雅山區上空重力波與紅色精靈	★ 科學酬載暉光剖面儀之實作與測試
★ 影像式光譜儀的實作與紅色精靈光譜研究	★ 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	★ 發展立方衛星搭載高光譜儀資料標準流程與校正實驗

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

Hyper-Spectral Camera Analyzer (Hyper-SCAN) 為本實驗室自行組裝之高光譜儀，計畫將於2022年搭載於scion微衛星太空任務中，特色為體積小、重量輕以及成本較低，其連續光譜帶範圍在464nm~676nm，擁有高光譜分辨率(~1nm)，此儀器以推掃式(push-broom)掃描獲取高光譜影像，視角大約為5.6度，在高度500公里的掃描幅寬為48.9公里，中心波長為570nm。

由於Hyper-SCAN仍在開發階段，獲取影像的過程中需要調整多種儀器，因此在本論文中提出一套取像實驗流程，並透過儀器的自動化控制減少整體實驗過程中的人工誤差與儀器操作時間。再者由於同時具有高空間解析度與高光譜解析度的光譜儀造價不斐且存在機構上的限制，因此文中使用耦合非負矩陣分解(CNMF)法融合高光譜影像資料與多光譜影像，同時亦對不同的初始端元(endmember)光譜作為演算法輸入所融合的結果進行比較。

摘要(英)

Hyper-Spectral Camera Analyzer (Hyper-SCAN) is a self-assembled hyperspectrometer in this laboratory. It is planned to be installed in the scion microsatellite space mission in 2022. It is characterized by small size, light weight and low cost. Its continuous spectrum The band range is 464nm~676nm, with high spectral resolution (~1nm), this instrument uses push-broom scanning to obtain hyperspectral images, the viewing angle is about 5.6 degrees, and the scan width at a height of 500 kilometers is 48.9 Kilometers, the center wavelength is 570nm.

Since Hyper-SCAN is still in the development stage, many instruments need to be adjusted in the process of acquiring images. Therefore, in this paper, a set of image acquisition experiment procedures is proposed, and the automatic control of the instruments reduces the manual error and instrument operation time in the overall experiment process. Furthermore, because spectrometers with both high spatial resolution and high spectral resolution are expensive and have institutional limitations, the coupled non-negative matrix factorization (CNMF) method is used to fuse hyperspectral image data and multispectral images. Different initial endmember spectra are used as input to the algorithm to compare the fusion results.

關鍵字(中)

★ 高光譜儀
★ 影像融合

關鍵字(英)

論文目次

摘要 ............................................i
Abstract ...................................... ii
目錄 .......................................... iii
圖目錄 ......................................... v
表目錄 ........................................ vii
一、緒論 ........................................ 1
二、高光譜儀 .................................... 4
2-1 高光譜遙測簡述 .............................. 4
2-2 Hyper-SCAN 成像基本原理 ..................... 6
2-3 Hyper-SCAN 系統規格與結構 ................... 8
三、高光譜影像處理原理 .......................... 11
3-1 各波段雜訊估計 ............................. 11
3-2 波段選擇 .................................. 12
3-2-1 協方差矩陣行列式法 ....................... 14
3-2-2 OIF 法 .................................. 14
3-3 特徵萃取 .................................. 15
3-3-1 PCA ..................................... 16
3-3-2 MNF ..................................... 17
3-4 端元提取 ................................... 18
3-4-1 PPI ..................................... 20
3-4-2 N-FINDR ................................. 21
3-5 高光譜圖像超解析技術 ........................ 22
四、Hyper-SCAN 拍攝流程 ........................ 27
4-1 硬體架設與拍攝方法 .......................... 27
4-2 資料處理 ................................... 32
五、拍攝結果分析與影像融合 ....................... 35
5-1 各波段雜訊估計 .............................. 37
5-2 特徵萃取 .................................... 39
5-3 端元提取 .................................... 43
5-4 高光譜與多光譜影像融合 ....................... 44
5-4-1 融合參數選擇 .............................. 45
5-4-2 融合結果 .................................. 46
5-4-3 融合品質評價指標 ........................... 48
六、結論 ........................................ 50
參考文獻 ........................................ 51

參考文獻

Abdi, Hervé, and Lynne J. Williams. "Principal component analysis." Wiley interdisciplinary reviews: computational statistics 2.4 (2010): 433-459.

Boardman, Joseph W. "Automating spectral unmixing of AVIRIS data using convex geometry concepts." (1993).

Carrasco, Oscar, et al. "Hyperspectral imaging applied to medical diagnoses and food safety." Geo-Spatial and Temporal Image and Data Exploitation III. Vol. 5097. International Society for Optics and Photonics, 2003.

Chang, Chein-I., and Antonio Plaza. "A fast iterative algorithm for implementation of pixel purity index."IEEE Geoscience and Remote Sensing Letters 3.1 (2006): 63-67.

Chang, Chein-I., and Qian Du. "Estimation of number of spectrally distinct signal sources in hyperspectral imagery." IEEE Transactions on geoscience and remote sensing 42.3 (2004): 608-619.

Chavez, P. S., Graydon L. Berlin, and Lynda B. Sowers. "Statistical method for selecting landsat MSS." J. Appl. Photogr. Eng 8.1 (1982): 23-30.

Craig, Maurice D. "Minimum-volume transforms for remotely sensed data." IEEE Transactions on Geoscience and Remote Sensing 32.3 (1994): 542-552.

Datasheet-Zaber motion library
https://www.zaber.com/software/docs/motion-library/ascii/references/matlab/

Feng, Yao-Ze, and Da-Wen Sun. "Application of hyperspectral imaging in food safety inspection and control: a review." Critical reviews in food science and nutrition 52.11 (2012): 1039-1058.

Filchev, Lachezar. "Satellite hyperspectral Earth observation missions-a review." Bulgarian Academy of Sciences. Space Research and Technology Institute, Aerospace Research in Bulgaria 26 (2014): 191-206.

Gomez, Richard B., Amin Jazaeri, and Menas Kafatos. "Wavelet-based hyperspectral and multispectral image fusion."Geo-Spatial Image and Data Exploitation II. Vol. 4383. International Society for Optics and Photonics, 2001.

Govender, Megandhren, K. Chetty, and Hartley Bulcock. "A review of hyperspectral remote sensing and its application in vegetation and water resource studies." Water Sa 33.2 (2007).

Green, Andrew A., et al. "A transformation for ordering multispectral data in terms of image quality with implications for noise removal." IEEE Transactions on geoscience and remote sensing 26.1 (1988): 65-74.

Gu, Yanfeng, and Ye Zhang. "Unsupervised subspace linear spectral mixture analysis for hyperspectral images." Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429). Vol. 1. IEEE, 2003.

Han, Xian-Hua, Boxin Shi, and Yinqiang Zheng. "Ssf-cnn: Spatial and spectral fusion with cnn for hyperspectral image super-resolution." 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018.

Hardie, Russell C., Michael T. Eismann, and Gregory L. Wilson. "MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor." IEEE Transactions on Image Processing 13.9 (2004): 1174-1184.

Kanatsoulis, Charilaos I., et al. "Hyperspectral super-resolution: A coupled tensor factorization approach." IEEE Transactions on Signal Processing 66.24 (2018): 6503-6517.

Khan, Muhammad Jaleed, et al. "Modern trends in hyperspectral image analysis: a review." IEEE Access 6 (2018): 14118-14129.

Lee, Daniel, and H. Sebastian Seung. "Algorithms for non-negative matrix factorize-tion." Advances in neural information processing systems 13 (2000): 556-562.

Lin, Chia-Hsiang, et al. "A convex optimization-based coupled nonnegative matrix factorization algorithm for hyperspectral and multispectral data fusion." IEEE Transactions on Geoscience and Remote Sensing 56.3 (2017): 1652-1667.

Nascimento, José MP, and José MB Dias. "Vertex component analysis: A fast algorithm to unmix hyperspectral data." IEEE transactions on Geoscience and Remote Sensing 43.4 (2005): 898-910.

Nielsen, Allan Aasbjerg. "Kernel maximum autocorrelation factor and minimum noise fraction transformations." IEEE Transactions on Image Processing 20.3 (2010): 612-624.

Shaw, Gary A., and Hsiaohua K. Burke. "Spectral imaging for remote sensing." Lincoln laboratory journal 14.1 (2003): 3-28.

Sheffield, Charles. "Selecting Band Combinations from Multi Spectral Data." Photogrammetric Engineering and Remote Sensing 58.6 (1985): 681-687.

Somers, Ben, et al. "Endmember variability in spectral mixture analysis: A review." Remote Sensing of Environment 115.7 (2011): 1603-1616.

Van der Meer, Freek D., and Steven M. De Jong, eds. Imaging spectrometry: basic principles and prospective applications. Vol. 4. Springer Science & Business Media, 2011.

Vasefi, F., N. MacKinnon, and D. L. Farkas. "Hyperspectral and multispectral imaging in dermatology." Imaging in Dermatology. Academic Press, 2016. 187-201.

Wei, Qi, et al. "Hyperspectral and multispectral image fusion based on a sparse representation." IEEE Transactions on Geoscience and Remote Sensing 53.7 (2015): 3658-3668.

Winter, Michael E. "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data." Imaging Spectrometry V. Vol. 3753. International Society for Optics and Photonics, 1999.

Yokoya, Naoto, Takehisa Yairi, and Akira Iwasaki. "Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion." IEEE Transactions on Geoscience and Remote Sensing 50.2 (2011): 528-537.

指導教授

郭政靈

審核日期

2021-1-28

推文