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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 徐百輝(Pai-Hui Hsu) | |
dc.contributor.author | Xiu-Man Huang | en |
dc.contributor.author | 黃琇蔓 | zh_TW |
dc.date.accessioned | 2021-06-17T00:56:17Z | - |
dc.date.available | 2016-08-22 | |
dc.date.copyright | 2011-08-22 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-17 | |
dc.identifier.citation | Aronoff, S., 2005. Remote sensing for GIS Managers, 1st edition, ESRI Press, New York Street, Redlands, California.
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F., 1968. On the Mean Accuracy of Statistical Pattern Recognizers, IEEE Transactions on Information Theory, 14(1): 55-63. Jain, A. K., R. P. W. Duin, and J. Mao, 2000. Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22: 4-37. Kruse, F. A., and A. B. Lefkoff, 1992. Hyperspectral imaging of the Earth's surface - an expert system-based analysis approach: in Proceedings of the International Symposium on Spectral Sensing Research, 15 - 20 November 1992, Maui, Hawaii, v. II, p. 1047 - 1062. Landgrebe, D. A., 2001. Analysis of Multispectral and Hyperspectral Image Data, Introduction to Modern Photogrammetry (E. M. Mikhail and J. S. Bethel, editors), Chris McGlone: John Wiley & Sons, Inc. Lee, C. and D. A. Landgrebe, 1993. Analyzing High-Dimensional Multisepctral Data, IEEE Transactions on Geoscience and Remote Sensing, 31: 792-800. Lei, Y. G. and M. J. Zuo, 2009. Fault Diagnosis of Rotating Machinery Using and Improved HHT Based on EEMD and Sensitive IMFs, Measurement Science and Technology, 20: 125701-12. Lillesand, T. M. and R. W. Kiefer, 2000. Remote Sensing and Image Interpretation, Fourth ed., New York: John Wiley & Sons. Linderhed, A., 2004. Adaptive Image Compression with Wavelet Packets and Empirical Mode Decomposition, PhD dissertation, Linköping University, Image Coding Group, 226 p. MultiSpec: A Freeware Multispectral Image Data Analysis System, 2010. Multispectral Data Analysis: A Signal Theory Perspective, URL: https://engineering.purdue.edu/~biehl/MultiSpec/, (last data accessed:2010/06/28). Peng, Z. K., P. W. Tse, F. L. Chu, 2005. A Comparison Study of Improved Hilbert-Huang Transform and Wavelet Transform, Mechanical Systems and Signal Processing, 19: 974-988. Piech, M. A. and K. R. Piech, 1987. Symbolic Representation of Hyperspectral Data, Applied Optic, 26: 4018-4026. Richards, J. A. and Xiuping Jia, 2006. Remote Sensing Digital Image Analysis: An Introduction, 4th ed, Springer-Verlag, Berlin Heidelberg, Germany. Roy, A., C.-H. Wen, J. F. Doherty and J. D. Mathews, 2008. Signal Feature Extraction form Microbarograph Observation Using the Hilbert-Huang Transform, IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1442-1447. Schlotthauer, G., M. E. Torres and H. L. Rufiner, 2010. Pathological Voice Analysis and Classification Based on Empirical Mode Decomposition, Development of Multimodal Interfaces: Active Listening and Synchrony (A. Esposito, N. Campbell, C. Vogel, A. Hussain and A. Nijholt, editors), Springer, Berlin/Heidelberg, pp. 364-381. Schowengerdt, R. A., 1997. Remote Sensing, Models and Methods for Image Processing, Second ed. San Diego: Academic Press. Sethu, V., E. Ambikairajah, J. Epps, 2008. Empirical Mode Decomposition Weighted Frequency Feature for Speech-Based Emotion Classification, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 31 March – 4 April 2008, Las Vegas, Nevada, pp. 5017-5020. Shaw, G.A. and H.-H.K. Burke, 2003. Spectral Imaging for Remote Sensing, Lincoln Laboratory Journal, 14(1): 3-28. Swain, P. H. and S. M. Davis, 1987. Remote Sensing: The Quantitative Approach, McGraw-Hill, New York. USGS Spectroscopy Lab, 1998. Imaging Spectroscopy Material Maps: Cuprite Introduction, URL: http://speclab.cr.usgs.gov/map.intro.html, (last data accessed:2011/07/14). Wu, Z. H., N. E. Huang, S. R. Long, and C.-K. Peng, 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series, Proc. Natl. Acad. Sci. USA., 104(38), 14889-14894. Wu, Z. H., N. E. Huang, 2009. Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method, Advance in Adaptive Data Analysis, 1(1): 1-41. Yuan, L., B. H. Yang, S. W. Ma and B. Cen, 2009. Combination of Wavelet Packet Transform and Hilbert-Huang Transform for Recognition of Continuous EEG in BCIs, 2009 2nd IEEE International Conference on Computer Science and Information Technology, 8-11 August 2009, Beijing China, pp. 594-599. 于德介、程軍聖、楊宇,2006。機械故障診斷的Hilbert-Huang變換方法,科學出版社,北京。 吳冠霖,2004。利用經驗解模法於高光譜資料之降為與光譜解析,國立成功大學資訊工程學系碩士論文。 徐百輝,2003。小波轉換應用於高光譜影像光譜特徵萃取之研究,國立成功大學測量工程學系博士論文。 徐百輝,2009。遙感探測課程講義。國立台灣大學,台北市。 國立中央大學數據分析方法研究中心,2010。Tutorial for the HHT MATLAB program,URL:http://rcada.ncu.edu.tw/ ,國立中央大學,(最後取得時間:2011/07/11)。 楊琇涵,2007。應用小波神經網路於高光譜影像分類,國立臺灣大學工學院土木工程學系碩士論文。 趙敏妏,2005。利用經驗解模法粹取空間上的頻率並將其應用在邊緣偵測與分類,國立成功大學資訊工程學系碩士論文。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66763 | - |
dc.description.abstract | 高光譜影像具有豐富且細緻的地物光譜資訊,有助於地物的判釋,並且提升影像分類之精度。然而受限於高光譜影像之高維度資料統計特性,在訓練樣本數不足的情況下,傳統以統計為基礎的影像分類方法並無法直接適用,這樣的問題一般稱為「維度的詛咒」。若在高光譜影像分類前,先透過適當的特徵萃取方法縮減高光譜影像的資料維度,將可有效解決「維度的詛咒」之問題。本研究應用Hilbert-Huang transform(HHT)方法於高光譜影像之光譜曲線分析與特徵萃取,以獲得有利於高光譜影像分類之光譜特徵。HHT是近年來新興發展的資料分析方法,其結合Empirical Mode Decomposition (EMD)與Hilbert Spectral Analysis (HSA)兩個演算法,可獲得時序性資料之瞬時頻率,常應用於具有非線性與非定常性特性之資料分析。本研究首先利用HHT之分解演算法EMD分析光譜曲線之吸收帶特徵,並且計算吸收帶之相關參數資訊,透過實際光譜曲線之實驗,顯示利用此方法確實可以有效的發現吸收帶之位置與計算相關參數資訊,未來將可用於光譜比對或地物判識;本研究並以吸收帶分析之結果為基礎,提出兩種以HHT為基礎之特徵萃取方法,透過光譜曲線之頻譜分析,由HHT之分量或是頻譜中根據特定的判斷準則萃取出有利於影像分類之光譜特徵,以解決有限訓練樣本和「維度的詛咒」之相關問題。研究中以兩組實際的高光譜像測試所提出的HHT方法應用於高光譜影像特徵萃取之有效性,實驗結果顯示利用HHT萃取出之光譜特徵確實可以降低高光譜影像之維度,並且保持影像分類之精度。 | zh_TW |
dc.description.abstract | Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. However, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as “curse of dimensionality”. The commonly used method to solve this problem is dimensionality reduction, and this can be done by feature extraction for hyperspectral images. In this study, the Hilbert-Huang transform (HHT) will be applied to hyperspectral image analysis. HHT, consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), is a relatively new adaptive time-frequency analysis tool and has been used extensively in nonlinear and nonstationary data analysis. In this study, the EMD is implemented on spectral curve for absorption band analysis firstly. The experiment results show that absorption features can be detected on IMF components effectively. The other objective of this study is to apply HHT on the hyperspectral data for physically spectral analysis. The spectral features are then extracted based on the results of physically spectral analysis, so that we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. Finally, two AVIRIS data sets are used to test the performance of the proposed HHT-based methods. According to the experiment results, the HHT-based methods are effective for dimensionality reduction and classification. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:56:17Z (GMT). No. of bitstreams: 1 ntu-100-R98521116-1.pdf: 4778301 bytes, checksum: b7cb6fe694297c3cd8ad976228f533cd (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III Abstract IV 目錄 V 圖目錄VIII 表目錄XI 第一章、前言 1 1.1研究背景與動機1 1.2相關文獻回顧2 1.3研究方法與流程4 1.4論文架構5 第二章、高光譜影像特徵萃取與分類7 2.1高光譜影像簡介7 2.1.1高光譜影像成像基本原理7 2.1.2維度的詛咒9 2.2影像分類與特徵萃取11 2.2.1影像分類11 2.2.2特徵萃取12 2.2.2.1主軸轉換法13 2.2.2.2判別分析特徵萃取法14 2.2.2.3決策邊界特徵萃取法15 2.2.2.4以小波轉換為基礎之特徵萃取16 2.3應用Hilbert-Huang Transform於高光譜影像分析之動機19 第三章、Hilbert-Huang Transform 21 3.1 Hilbert-Huang Transform簡介21 3.2 Empirical Mode Decomposition 22 3.2.1方法介紹22 3.2.2 Empirical Mode Decomposition之特性25 3.2.3 Empirical Mode Decomposition之模擬實驗26 3.2.4 Ensemble Empirical Mode Decomposition 29 3.3 Hilbert Spectral Analysis31 3.3.1方法介紹31 3.3.2 Hilbert Spectral Analysis之特性32 3.3.3 Hilbert Spectral Analysis之模擬實驗32 3.4 Hilbert-Huang Transform相關文獻回顧34 3.5 Hilbert-Huang Transform與小波轉換之比較36 3.5.1理論基礎之比較36 3.5.2以模擬訊號比較Hilbert-Huang transform與小波轉換37 3.5.3以實際訊號比較Hilbert-Huang transform與小波轉換41 第四章、應用Hilbert-Huang Transform於光譜吸收帶分析 45 4.1光譜吸收帶簡介45 4.2應用Empirical Mode Decomposition之光譜吸收帶分析46 4.3實驗52 4.3.1不同類別之葉綠素吸收帶分析52 4.3.2光譜吸收帶資訊之影像54 4.4結論56 第五章、應用Hilbert-Huang Transform於特徵萃取57 5.1採用區域極值之特徵萃取57 5.1.1實驗方法介紹57 5.1.2測試一:IMF的分量數對特徵萃取與分類成果之影響60 5.1.3測試二:採用不同特徵排序方式對於特徵萃取與分類成果之影響65 5.1.4 採用Cuprite礦區影像資料之實驗68 5.2採用Hilbert spectrum之特徵萃取71 5.2.1實驗方法介紹71 5.2.2測試一:IMF的分量數對於特徵萃取與分類成果之影響73 5.2.3測試二:採用不同特徵排序方式對於特徵萃取與分類成果之影響76 5.2.4採用Cuprite礦區影像資料之實驗78 5.3實驗80 5.3.2實驗一:不同訓練樣本數對於特徵萃取與分類成果之影響80 5.3.3實驗二:以監督式之特徵萃取對於分類成果之影響85 5.3.4實驗三:不同特徵萃取方法之比較88 5.4結論90 第六章、結論與建議93 6.1結論93 6.2未來工作94 參考文獻 95 | |
dc.language.iso | zh-TW | |
dc.title | 應用Hilbert-Huang Transform於高光譜影像分析 | zh_TW |
dc.title | Hyperspectral Image Analysis Using Hilbert-Huang Transform | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 邱式鴻,詹進發,趙鍵哲 | |
dc.subject.keyword | Hilbert-Huang Transform,高光譜影像,光譜吸收帶,特徵萃取,分類, | zh_TW |
dc.subject.keyword | Hilbert-Huang Transform,Hyperspectral Image,Absorption Band,Feature Extraction,Classification, | en |
dc.relation.page | 101 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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