博碩士論文 945402019 詳細資訊




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姓名 王詠令(Yung-Ling Wang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 經驗模式分解應用於高光譜資料分析
(Empirical Mode Decomposition for Hyperspectral Data Analysis)
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摘要(中) 光學遙測利用物質的反射特性進行辨識,因為不同物質具有獨特的吸收帶而形成一種獨特的光譜特徵,運用此獨特性可以根據光譜辨別不同的物質來進行分類。傳統光譜辨別的方式,是直接測量光譜之間的距離或角度作為相似度分析,然而實際的光譜通常包含雜訊的干擾,傳統的測量方法沒有足夠的容錯能力而造成誤差。本研究提出一種新的方法來測量光譜辨別物質之間的相似性,我們採用經驗模式分解來將光譜分解成幾個本質分量,並使用這些分量以提高光譜辨識的性能。從分解的分量中發現,訊號與雜訊被區分在不同的分量,而吸收區資訊分散於前面數個分量中,這些分量具有更好的能力來辨別物質。為了方便評估,我們提出幾種常用的測量的方法來進行性能比較分析,如歐氏距離、光譜角度和馬氏距離。本實驗的樣本光譜是由美國地質調查局(USGS)的光譜庫提供,實驗結果證明經驗模式分解後的光譜相似性測量,能更有效地萃取光譜特徵,提升分類準確性。
摘要(英) Optical remote sensing can distinguish different materials because each material has its own unique absorption characteristics to form a unique spectrum. This information can be adopted to discriminate different materials in optical remote sensing images. Traditional approach for spectra similarity measurement is calculating the Euclidean distance or spectral angle between two spectra directly. However, in reality the spectra usually contain noise or interference which cannot be tolerated by traditional measurements. In this study, we propose a new approach to measure the similarity between the spectra to discriminate materials. It adopts Empirical Mode Decomposition (EMD) to decompose the spectrum into several components, called Intrinsic Mode Functions (IMFs). The absorption features are highlighted and the noise is reduced in the first few IMFs, so the ability of material discrimination is improved. For evaluation purpose, we compare the proposed method with several commonly used measurements, including Euclidean distance, Spectral Angle and Mahalanobis distance. The sample spectra used for experiment are provided by the spectral library of U. S. Geological Survey (USGS). The experiments results have demonstrated that EMD can extract the spectral features more effectively than common spectral similarity measurements and improve the classification performance.
關鍵字(中) ★ 高光譜
★ 經驗模式分解
★ 歐氏距離
★ 光譜角度
★ 馬氏距離
關鍵字(英) ★ Hyperspectrum
★ Empirical Mode Decomposition (EMD)
★ Euclidean distance
★ Spectral Angle
★ Mahalanobis distance
論文目次 Contents
摘 要 V
ABSTRACT VI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Overview of Dissertation 4
1.3 Data used for dissertation 9
1.4 Organization of the Dissertation 11
Chapter 2 Discriminated Hyperspectral image by EMD 12
2.1 Introduction of EMD 12
2.2 Distance Measure 16
2.3 Hyperspectral data 17
2.4 Original hyperspectral data discriminate similariry 18
2.5 Experiment results 19
2.6 Summary 28
Chapter 3 Discriminated hyperspectral image by EEMD 29
3.1 Introduction ensemble EMD (EEMD) 29
3.2 Simulated Hyperspectral data 32
3.3 Kappa coefficient 33
3.4 Experimental Result 34
3.5 Summary 49
Chapter 4 Graphics Processing Units 51
4.1 Background 51
4.2 principle of GPU 53
4.3 GPU and EEMD relationship 54
4.4 Summary 55
Chapter 5 Experimental Results 57
5.1 SNR = 20 to simulate for comparison of IMF by EEMD 57
5.2 Original spectral data with SNR = 30 to simulate for comparison of IMF by EEMD 66
5.3 Original spectral data added SNR = 40 noise to simulate for comparison of IMF by EEMD 74
5.4 Summary 82
Chapter 6 Conclusions and Further works 84
6.1 Conclusions 84
6.2 Further works 84
References 86
參考文獻 [1]. Chein-I Chang, “Hyperspectral Imaging”, Kluwer Academic/Plenum Publishers. 2003
[2]. Christoph Hecker, Mark van der Meijde, Harald van der Werff, and Freek D. van der Meer, “Assessing the Influence of Reference Spectra on synthetic SAM Classification Results”, IEEE Transactions of Geoscience and Remote Sensing, Volume 46, Issue 12, December 2008, Pages 4162-4172.
[3]. Nirmal Keshava, “Distance Metrics and Band Selection in Hyperspectral Processing With Application to Material Identification and Spectral Libraries”, IEEE Tranactions of Geoscience and Remote Sensing, Volume 42, Issue 7, July 2004, Pages 1552-1565.
[4]. Sebastian van der Linden , Bjorn Waske and Patrick Hostert, ”Towards an Optimized Use of The Spectral Angle Space”, Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, 23-25 April 2007, Pages 1-5.
[5]. Yuhas, Roberta H. Goetz, Alexander F. H. Boardman, Joe W., “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm”, Summaries of the Third Annual JPL Airborne Geoscience Workshop, Volume 1, June 1992, Pages 147-149.
[6]. Rully Soelaiman, Dommy Asfiandy, Yudhi Purwananto, Mauridhi H. Purnomo, ”Weighted kernel function implementation for hyperspectral image classification based on Support Vector Machine”, 2009 International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), Bandung, 23-25 November 2009, Pages 1-6.
[7]. Bjorn Waske, Jon Atli Benediktsson, Kolbeinn Arnason and Johannes R. Sveinsson,” Mapping of Hypersprectral AVIRIS data using machine-learning algorithms”, Canadian Journal of Remote Sensing, Volume 35, Issue S1, 2009, Pages S106-S116.
[8]. Fred A. Kruse,”Expert System Analysis of Hyperspectral Data”, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, Proceedings of SPIE, Volume 6966, April 2008.
[9]. Yiping Xu, Kaoning Hu and Jianxin Han, “Classification based on the EMD of hyperspectral curve”, Proceedings of SPIE Second International Conference on Space Information Technology, Volume 6795, November 2007.
[10]. Norden E. Huang, Zheng Shen, Steven R.Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung and Henry H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Proceedings of the Royal Society London A., volume 454, Issue 1971, March 1998, Pages 903-999.
[11]. Norden E. Huang, Steven R. Long, Zheng Shen, "The Mechanism for Frequency Downshift in Nonlinear Wave Evolution". Advances in Applied Mechanics, vol. 32, 1996, Pages 59-111.
[12]. Norden E. Huang, Man-li C. Wu, Steven R. Long, Samuel S.P Shen, Wendong Qu, Per Gloersen, and Kuang L. Fan, “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectrum Analysis”, Proceedings of the Royal Society London A., Volume 459, Issue 2037, September 2003, Pages 2317-2345.
[13]. Zhaohua Wu and Norden E. Huang, “Ensemble empirical mode decomposition: A noise-assisted data analysis method”, Advances in Adaptive Data Analysis, Volume 1, Issue 1, January 2009, Pages 1-41.
[14]. Liang Chen, Xing Li, Xun-bo Li, Zuo-ying Huang, “Signal extraction using Ensemble Empirical Mode Decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation” Review of Scientific Instruments, Volume 80, Issue 2, February 2009, Pages 025105 – 025106.
[15]. Begum Demir, and Sarp Erturk, “Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification”, IEEE Transactions on Geoscience and Remote Sensing, Volume 48, Issue 11, November 2010, Pages 4071-4084.
[16]. Zhaohua Wu, Norden E. Huang and Xianyao Chen, “The multi-dimensional ensemble empirical mode decomposition method”, Advances in Adaptive Data Analysis, Volume. 1, Issue 3, July 2009, Pages 339-372.
[17]. Anna Linderhed,”Image Empirical Mode Decomposition : A new tool for image processing”, Advances in Adaptive Data Analysis, Volume 1, Issue 2, April 2009, Pages 265–294.
[18]. Zhaohua Wu and Norden E. Huang, “A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method”, Proceedings of the Royal Society London A., Vol. 460, No. 2046, June 2004, Pages 1597–1611.
[19]. Begum Demir, Sarp Erturk, and M. Kemal Gullu,”Hyperspectral image classification using denoising of intrinsic mode functions”, IEEE geoscience and remote sensing letters, Volume. 8, Issue 2, March 2011, Pages 220-224
[20]. Li-Wen Chang, Men-Tzung Lo, Nasser Anssari, Ke-Hsin Hsu, Norden E. Huang and Wen-mei W. Hwu, “Parallel implementation of multi-dimensional ensemble empirical mode decomposition”, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 22-27 May 2011, Pages 1621-1624.
[21]. Alexandros Karagiannis and Philip Constantinou,” Noise-Assisted Data Processing with Empirical Mode Decomposition in Biomedical Signals”, IEEE transactions on information technology in biomedicine, Volume 15, Issue 1, January 2011, Pages 11-18.
[22]. Dan Chen, Duan Li, Muzhou Xiong, Hong Bao, and Xiaoli Li, “GPGPU-aided ensemble empirical mode decomposition for EEG analysis during anaesthesia”, IEEE Transaction on Information Technology in Biomedicine, Volume 14, November 2010, Pages 1417-1427.
[23]. N. Abdolmaleki and M. Pooyan,” Source separation from single channel biomedical signal by combination of blind source separation and empirical mode decomposition”, International Journal of Digital Information and Wireless Communications (IJDIWC), Volume 1, January 2012, Pages 887-893.
[24]. Md. Khademul, Islam Molla, Akimasa Sumi and M. Sayedur Rahman,” Analysis of Temperature Change under Global Warming Impact using Empirical Mode Decomposition”, International Journal of Information Technology, Volume 3 Issue 2, April 2007, Page 131
[25]. Kais Khaldi, Monia Turki-Hadj Alouane and Abdel-Ouahab Boudraa,” A new EMD denoising approach dedicated to voiced speech signals”, 2008 International Conference on Signals, Circuits and Systems, IEEE, Monastir, 7-9 November 2008, Pages 1-5.
[26]. Kopsinis, Y. and McLaughlin, S., “Development of EMD-based Denoising Methods Inspired by Wavelet Thresholding”, IEEE Transaction on Signal Processing, Volume 57, Issue 4, April 2009, Pages 1351-1362.
[27]. Kais Khaldi at. el.” speech signal noise reduction by EMD”, International symposium on communications, control and signal processing (ISCCSP), 2008, Malta, 12-14 March 2008, Pages 1155-1158
[28]. Jose Chilo, Jason M. Kinser and Thomas Lindblad,” Discrimination of Nuclear Explosions Sites by Seismic Signals using Intrinsic Mode Functions and Multi-Modal Data Space”, International Geoscience and Remote Sensing Symposium 2008 (IGARSS 2008), IEEE, Boston, MA, 7-11 July 2008, Volume II, Pages 895-898.
[29]. Yan-Ping Liu, Yue Li, Hai-Tap Ma,” seismic random noise reduction by empirical mode decomposition combined with translation invariant scale-adaptive threshold”, the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July 2012, Pages 53-57.
[30]. Helong Li, Xiaoyan Deng, Hongliang Dai,” Structural damage detection using the combination method of EMD and wavelet analysis”, Mechanical Systems and Signal Processing, Volume 21, Issue 1, January 2007, Pages 298–306.
[31]. Yinfenga Dong, Yingmina Li, Mingb Lai,” Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model”, Soil Dynamics and Earthquake Engineering, volume 30, Issue 3, March 2010, Pages 133–145.
[32]. http://speclab.cr.usgs.gov/spectral.lib04/clark1993/spectral_lib.html#Tbl2.
[33]. Clark, R.N., G.A. Swayze, A.J. Gallagher, T.V.V. King, and W.M. Calvin, 1993, The U. S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns, U.S. Geological Survey Open File Report 93-592, 1340 pages, http://speclab.cr.usgs.gov.
[34]. http://aviris.jpl.nasa.gov/aviris/spectrum.html
[35]. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) homepage, http://aviris.jpl.nasa.gov/.
[36]. David L. Donoho, “De-noising by soft-thresholding”, IEEE Transactions on Information Theory, Volum. 41, Issue 3, May 1995, Pages 613-627.
[37]. Patrick Flandrin, Gabriel Rilling, and Paulo Goncalves,”Empirical mode decomposition as a filter bank”, IEEE Signal Processing Letters, vol. 11, February 2004, Pages 112-114.
[38]. Gabriel Rilling, Patrick Flandrin and Paulo Goncalves, “On empirical mode decomposition and its algorithm”, IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing, 2003, Pages 8-11.
[39]. Gabriel Rilling and Patrick Flandrin,”One or two frequencies? The Empirical Mode Decomposition answers”, IEEE Transaction Signal Processing, Volume 56, Issue 1, January 2008, Pages. 85-95.
[40]. Gerald Keller, “Statistics-For Management and Economics”, Thomson, 2005.
[41]. Jean Carletta, “Assessing agreement on classification tasks: The kappa statistic”, Journal of Computational Linguistics, Volume 22, June 1996, Pages 249-254.
[42]. NVIDIA(2009). “CUDA Programming Guide v3.2”, NVIDIA.
指導教授 任玄(Hsuan Ren) 審核日期 2014-6-19
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