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論文中文名稱:以多重解析度分析研探數值地形模型之特徵萃取 [以論文名稱查詢館藏系統]
論文英文名稱:A Study of Feature Extraction in Multi-Resolution Analysis for DTM [以論文名稱查詢館藏系統]
英文姓名:Jyu- Fong Lai
指導教授英文名:Che-Hao Chang
口試委員英文名:Li-hsien Chen;Miao-Hsiang Peng;Kuo-Chen Chang
英文關鍵詞:Empirical Mode DecompositionFeature extractionMulti-Resolution AnalysisDigital Terrain Model
論文中文摘要:數值地形模型(Digital Terrain Model, DTM)係以數字形式儲存描述地表特徵及空間分佈的數值集合。近年來,獲得高解析度DTM技術已逐漸成熟,使得三維空間之立體景觀模擬可栩栩如生地展現在使用者眼前。然於實務應用層面,當展示大範圍與低複雜度之數值地形時,高解析度與高精度DTM資料不僅增加儲存空間,更降低其處理效率。因此,針對觀看距離,選擇適當解析度之數值地形模型予以呈現,即為多重解析度分析(Multi-Resolution Analysis, MRA)之概念,亦為近年來相當重要之研究議題。
本研究應用希伯特黃轉換(Hilbert-Huang Transform, HHT)中之經驗模態分解法(Empirical Mode Decomposition, EMD),可將DTM分解成數個IMF分量。本研究提出兩種特徵萃取方法,從IMF分量得到DTM特徵位置,即可作為多重解析度分析之依據。特徵萃取法之一稱為低差異法:以IMF獲得主趨勢面,與原始高程相減後,差異量小者之高程位置為萃取依據。方法之二稱為高振幅法:以IMF中,超過振幅門檻值之位置,定為特徵點高程位置。研究中並結合Surfer 8.0中之交叉驗證法,萃取地形特徵點並與兩種特徵萃取方法進行比對分析。
由實驗結果顯示,三種特徵萃取方法經美國攝影測量及遙感探測學會之地圖精度標準(ASPRS, American Society for Photogrammetry Remote Sensing )驗證後,皆符合精度要求。當壓縮率介於5%至25%時,三種特徵萃取方法中以高振幅法之高程精度較佳。然而,當壓縮率大於25%時,交叉驗證法之精度將會高於高振幅法。
論文英文摘要:Digital Terrain Model is stored in digital form to describe surface feature and the spatial distribution of Numerical collection. In recent years, to obtain high-resolution DTM technology has gradually matured, makes the stereo visual simulation of three-dimensional space can be show vivid to the user. However, in the practical application , when displaying a wide range and the low complexity of digital terrain model, High-resolution and high accuracy DTM data is not only to increase storage space but also reduce its processing efficiency. Therefore, for the viewing distance, selecting the appropriate resolution of the digital terrain model to display, it is the concept of Multi-Resolution Analysis. This research topic is also very important recently.
This study applied Empirical Mode Decomposition of Hilbert-Huang Transform, DTM can be decomposed into a number of IMF components. This study proposed two kinds of feature extraction method. We obtained DTM feature position from IMF components. It can be as a basis for Multi-resolution Analysis. One of feature extraction method is extraction point at low trend difference method: It is obtain the main trend surface from IMF, after subtracting the original elevation to select the smaller difference of the elevation poistion. Another of feature extraction method is extraction point at high amplitude: according to IMF, the location of more than amplitude threshold is set to feature point. This study combined with cross validation method to compare with the two kinds of extraction methods.
From the experimental results, the three feature extraction methods verified by the map accuracy standards of American Society for Photogrammetry Remote Sensing that are in compliance with accuracy requirements.
When the compression rate is between 5-25%, in the three kinds of feature extraction methods, the best method is extraction point at high amplitude. However, when the compression rate is more than 25%, the accuracy of cross validation will better than the extraction point at high amplitude method.
論文目次:摘要 i
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究目的 1
1.2 文獻回顧 2
1.2.1 數值高程模型相關研究 2
1.2.2 多重解析度分析之應用 3
1.2.3 HHT相關研究背景 3
1.3 研究內容 6
第二章 DTM特徵萃取理論與方法 8
2.1 希爾伯特-黃轉換理論基礎 8
2.1.1 經驗模態分解法與內建模態函數 10
2.2多重解析度基本概念 15
2.3低差異萃取法 20
2.4高振幅萃取法 21
2.5交叉驗證法 22
第三章 多重解析地形重建 24
3.1極值篩選 24
3.2特徵萃取法 30
3.3二維重建 34
3.4 交叉驗證法之實驗程序 36
第四章 地形精度評估 38
4.1研究區數值地形探討 38
4.2壓縮率之定義與精度評估指標 39
4.3低差異法與高振幅法之成果分析 42
4.4 三種特徵萃取法之比較 52
第五章 結論與建議 56
5.1 結論 56
5.2 建議 57
參考文獻 58
論文參考文獻:[1] A.Linderhed, “2-D empirical mode decomposition – in the spirit of image compression ” , in Wavelet and Independent Component Analysis Applications IXI , vol. 4738 , pp.1-8 , April 2000.
[2] A.Linderhed, “Adaptive Image Compression with Wavelet Packets and Empirical Mode Decomposition” , Linkoping studio in science and technology dissertation , No. 909 , 2004.
[3] C. Y. Lo , L. C. Chen , “Canopy Extraction Using Airborne Laser Scanning Data in Forestry Areas ” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , Vol. XXXVII , Part B3b ,2008.
[4] H.C. North and Q.X. Wu, “Multiscale edge detection and thresholding in SAR imagery,” in Proc. Image and Vision Computing, pp. 247–252, Aug. 1999.
[5] H.Hariharan, et al, “Image Fusion and Enhancement via Empirical Mode Decomposition”, Journal of Pattern Recognition Research, vol.12, no.36, pp.18-32, 2006.
[6] J. C. Nunes , Y. Bouaoune , E. Delechelle , O. Niang , and P. Bunel , “Image analysis by bidimensional empirical mode decomposition ” , Image and Vision Computing , vol.21 , pp. 1019-1026 , 2003.
[7] J. Canny, “A Computational Approach to Edge Detection,’’ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, 1986.
[8] J. P. Chiles, P. Delfiner, “Geostatistics: Modeling Spatial Uncertainty. John Wiley and Sons, New York, pp. 695, 1999.
[9] J.Höhle , M.Höhle , “Trend extraction using average interpolating subdivision” Mathematical Geology , Vol.37, No.6, Journal of Photogrammetry and Remote Sensing , 2005.
[10] J.Höhlea, M.Höhle , “Accuracy assessment of digital elevation models by means of robust statistical methods” , ISPRS Journal of Photogrammetry and Remote Sensing,pp.398-406,2009
[11] J.N. Yang, Y. Lei, S. Lin, N. Huang, “Hilbert-Huang Based Approach for Structural Damage Detection,” Journal of Engineering Mechanics, Vol. 130, No. 1, pp 85-95, 2004.
[12] J.Pouderoux, J.E.Marvie, “Adaptive streaming and rendering of large terrains using strip masks”, Proceedings of the 3rd International Conference on Computer Graphics and Interactive Techniques, pp. 299-306, 2005.
[13] J.R.Kim,J.P.Muller, “Multi-resolution topographic data extraction from Martian stereo imagery” , Planetary and Space Science, pp. 2095-2112, 2009
[14] J.T. Bjørke and S.Nilsen , “Wavelets applied to simplification of digital terrain models” , Geographical Information Science , 2003.
[15] L.D.Floriani, “Regular and irregular multi-resolution terrain models: a comparison”, Proceedings of the 10th ACM international symposium on Advances in geographic information systems, pp. 143-148, 2002.
[16] L.Liang, Z.Ping, “An Edge Detection Algorithm of Image Based on Empirical Mode Decomposition” 2nd International Symposium on Intelligent Information Technology Application no. 4739549, pp. 128-132, 2008.
[17] N.E. Huang , M.C. Wu, S.R. Long, S.S.P. Shen, W. Qu, P. Gloersen & K.L. Fan, “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis ” ,Proc. R. Soc. Lond. A 4549, pp. 2317-2345, 2003.
[18] N.E.Huang, Z.Shen, S.R.Long, et al. “The Empirical Mode Deomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. Roy. Soc, London. A, vol.454, pp.903-995, 1998.
[19] P. Cignoni, “Representation and Visualization of Terrain Surfaces at Variable Resolution”, IST, 1997.
[20] P. Flandrin, G. Rilling, P. Goncalves, “Empirical Mode Decomposition as a Filter Bank”, IEEE Signal Processing Letters, Vol. 11, No. 2, pp. 112-114 , 2004.
[21] R. Öktem, L. Öktem, K. Egiazarian, “A wavelet packet transform based image coding algorithm”, IEEE Nordic Signal Processing Symposium , pp. 77-80 , 2000.
[22] S.G.Mallat, “A theory for multiresolution signal decomposition: the wavelet representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence,1989.
[23] S.H.Chen, H.B.Su, R.H. Zhang, J.Tian, “ Fusing remote sensing images using a trous wavelet transform and empirical mode decomposition ”, Pattern Recognition Letters, Vol.29, pp.330-342 , 2008.
[24] Saffet Erdoğan , “Modelling the spatial distribution of DEM error with geographically weighted regression: An experimental study” , Computers and Geosciences, pp.34-43,2010
[25] T.D.DeRose, “Multiresolution Analysis for Surfaces of Arbitrary Topological Type”, ACM Transactions on Graphics, Vol.16, No.1, pp. 34 – 73, 1997.
[26] Y. Huanyin, “A SAR Interferogram Filter Based on the Empirical Mode Decomposition Method”, International Geosciences and Remote Sensing Symposium, pp. 2061-2063 , Australia , 9-13 July 2000,
[27] Z. F. Liu, Z.P. Liao, E. Sang, “Noise removal of sonar images using empirical mode decomposition,” Proc. of SPIE, Vol.6044, 2005.
[28] Z.X.Liu, “Texture Segmentation Using Directional Empirical Mode Decomposion”, International Conference on Image Processing, pp.279-282, 2004.
[29] Z.X.Liu, H.J.Wang, and S. L.Peng, “Texture classification through directional empirical mode decomposition”, Int. Conf. Pattern Recognition, vol. 4 , pp. 803-806 , 2004.
[30] 江鴻鑫,經驗模態分解法應用於數值地形模型資料分析之研究,碩士論文,國防大學中正理工學院- 軍事工程研究所,碩士論文,2006
[31] 余翠紋,高解析度數值地形模型精度評估與應用,碩士論文,國立成功大學地球科學系碩士班,2004.
[32] 吳冠霖,利用經驗解模法於高光譜資料之降維與光譜解析,國立成功大學資訊工程系,碩士論文,2004
[33] 吳紹禎,不同時期空載光達成果比對探討–以新竹地區為例,碩士論文,國立交通大學土木工程系所,2006
[34] 連翊涵,區塊式Level of Detail地景視覺模擬之研究,碩士論文,國立中央大學-土木工程研究所,碩士論文,2006
[35] 陳冠融,以經驗模態分解法消除二維影像之雷射光斑之可行性分析,碩士論文,國立中山大學光電工程研究所,2007
[36] 彭淼祥,空載光達生產數值高程模型及其精度評估,博士論文,國立交通大學-土木工程系所,2006
[37] 曾志豪,應用HHT於軌道結構分析之研究,中原大學土木工程學系,碩士論文,2006
[38] 楊善智,數值高程模型(DEM)之品質評估,碩士論文,國立成功大學-測量及空間資訊學系,2007
[39] 趙敏妏,利用經驗解模法萃取空間的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系,碩士論文,2005
[40] 蔡宗勳,數值高度模型之地形量度研究,國立台灣大學-地理學系,碩士論文, 1994
[41] 顏宏宇,LiDAR直接量測數值地形資料精度分析與應用,碩士論文,國立成功大學-地球科學系碩士班,2005