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研究生:胡孟杰
研究生(外文):Meng-Jye Hu
論文名稱:數位遙測影像之強化、壓縮、轉換、分類與目標偵測
論文名稱(外文):Digital Remote Sensing Image Analysis: Enhancement, Compression, Transformation, Classification, and Target Detection
指導教授:貝蘇章
指導教授(外文):Soo-Chang Pei
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:120
中文關鍵詞:遙測影像虹膜分類目標偵測壓縮
外文關鍵詞:remote sensing imageretinexcompressionclassi
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隨著太空科技與衛星偵測技術的進步,遙測影像(remote sensing image)已經被廣泛的應用於各種民生與軍事的用途。遙測影像具有全面監測廣大區域、多頻譜(multispectral)與可重複觀察等特性,對於地表的監測、生態環境的變遷、地理資訊的蒐集等應用有很大的幫助。
從影像處理的觀點來看,遙測影像與傳統彩色影像最大的不同在於多頻譜(multispectral)的特性。相較於一張彩色影像可分為R、G、B三個部分,遙測影像通常涵遞ぉ蚗W帶(bands),而每個頻帶都是一張灰階的影像。舉例來說,LNDSAT衛星MSS影像包括了7張灰階影像,而AVIRIS影像則包括了224張不同頻帶的影像。
AVIRIS這類包括數百個頻帶的遙測影像,通常被稱為超頻譜(hyperspectral)影像。相較於多頻譜影像,超頻譜影像有些不太尋常的特性,尤其是在壓縮與分類方面,這些問題影響特別重要。
這篇論文主要是討論一些與遙測影像相關的應用,包括影像強化、壓縮、轉換、分類與目標偵測。而我們的討論將著重於超頻譜影像的高維(high dimensional)特性,線性轉換(linear transformation)的應用、與不同目標偵測演算法的比較。而實驗結果顯示,頻譜的轉換可用來解決高維影像產生的問題,而分段的(segmented)線性轉換則可以改進壓縮與辦識的結果。
In this paper, we discuss several applications of remote sensing image analysis. Unlike the traditional color image, remote sensing image is acquired by the sensor comprises a number of bands, each of which represents the intensity of an imaged scene that is received by a sensor at a particular wavelength. In other words, it is either a multispectral or a hyperspectral image.
The applications of remote sensing image we discussed in this paper including image enhancement technology, spectral domain transformation, image compression, classification, spectral unmixing, and subpixel target detection. Our discussion is focus on the unusual and unintuitive characteristics of hyperspectral image comparing with multispectral image for data compression and classification, the application of spectral domain linear transformations, especially the segmented based transformation, and the performance of the different target detection algorithms.
The experiment results shows that spectral domain linear transformation can be used to solve the problem that is caused by the high dimensional data set and the segmented based transformation can greatly improve the compression ratio and classification accuracy of hyperspectral imagery.
CHAPTER 1 Introduction 1

CHAPTER 2 Fundamentals of Remote Sensing Image Data 5
2.1 Introduction 5
2.2 Models of Remote Sensing System 6
2.2.1 Radiation Source 6
2.2.2 Sensor Model 10
2.2.3 Data system 11
2.3 Types of Remote Sensing Data 12
2.4 Characteristics of Digital Remote Sensing Image Data 14
2.4.1 Parameters of Digital Remote Sensing Image Data 14
2.4.2 Different Types of Remote Sensing Image Data 15
2.5 Conclusion 18

CHAPTER 3 Enhancement Technologies of Image Data 19
3.1 Introduction 19
3.2 Retinex Algorithm 20
3.2.1 Signal-Scale Retinex 21
3.2.2 Multi-Scale Retinex 22
3.3 Comparison of Different Retinex Algorithms 25
3.3.1 Multi-scale Retinex Color Restoration (MSRCR) 25
3.3.2 Chromaticity Preserving MSR (CPMSR) 27
3.3.3 Adaptive Scale-Gain MSR 29
3.4 Conclusion 33

CHAPTER 4 Multispectral / Hyperspectral Transformations of
Image Data 35
4.1 Introduction 35
4.2 The Principal component Analysis 36
4.2.1 The Mean Vector and Covariance Matrix 36
4.2.2 Principal component Analysis 37
4.3.3 Correlation Image and Segmented Principal component Analysis 43
4.3 Discrete Cosine Transform 45
4.4 Independent Component Analysis 46
4.5 Conclusion 51

CHAPTER 5 Compression of Hyperspectral Imagery 53
5.1 Introduction 53
5.2 Compression by Vector Quantization 54
5.2.1 Vector Formation 55
5.2.2 Training Set 56
5.2.3 Codebook Generation 57
5.2.4 Quantization and Compression of the Residual Image 58
5.3 Compression by Spectral Domain Decorrelation Transform 59
5.4 Experimental Results and Discussion 60
5.5 Conclusion 66



CHAPTER 6 Classification Techniques of Remote Sensing
Image Data 69
6.1 Introduction 69
6.2 Supervised Classification 70
6.2.1 Maximum Likelihood Classification 71
6.2.2 Number of Training Pixels and Feature Extraction 73
6.3 Unsupervised Classification 74
6.4 Cluster Space Classification 75
6.5 Experiments 76
6.5.1 Ground Truth of 92AV3C 77
6.5.2 Confuse Matrix 79
6.5.3 Classification Results 79
6.6 Conclusion 85

CHAPTER 7 Spectral Unmixing and Target Detection 87
7.1 Introduction 87
7.2 Spectral Unmixing 88
7.2.1 Linear Mixing Model 89
7.2.2 End-member Selection 90
7.2.3 Inversion 92
7.2.4 Experiments 94
7.3 Target Detection 96
7.3.1 Orthogonal Subspace Projection 97
7.3.2 Noise Subspace Projection 99
7.4 Experimental Results 101
7.4.2 Experiments of NSP 103
7.5 Conclusion 106

CHAPTER 8 Conclusion and Future Works 109
8.1 Conclusion 109
8.2 Future Works 111
8.2.1 Future Works of Hyperspectral Image Compression 111
8.2.2 Future Works of Classification 112

Reference 113
Reference



Chapter 2 Fundamentals of Remote Sensing Image Data
[1]J. A. Richards, Xiuping Jia, “Remote Sensing Digital Image Analysis – An Introduction”, 3rd edition, Springer
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Chapter 3 Enhancement Technologies of Image Data
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Chapter 4 Multispectral / Hyperspectral Transformations of Image Data
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Chapter 5 Compression of Hyperspectral Imagery
[22]R. E. Roger, M. C. Cavenor, “Lossless Compression of AVIRIS Images”, IEEE Transactions on Image Processing, Vol. 5, No.5, pp.713-719, May 1996.
[23]M. J. Ryan, J. F. Arnold, “The Lossless Compression of AVIRIS Images by Vector Quantization”, IEEE Transaction on Geosciences and Remote Sensing, Vol. 35, No.3, May 1997
[24]M. R. Pickering, M.J. Ryan, “Compression of Hyperspectral Data Using Vector Quantization and the Discrete Cosine Transform”, 2000 International Conference on Image Processing, Vol. 2, 10-13, pp.195 - 198, Sept. 2000
[25]H. S. Lee, N. H. Younan, R. L. King, “Hyperspectral Image Cube Compression Combining JPEG-2000 and Spectral Decorrelation”, 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ) Vol. 6 , 24-28, 66. 3317 – 3319, June 2002
[26]M. D. Pal, C. M. Brislawn, “Feature Extraction From Hyperspectral Images Compressed Using The JPEG-2000 Standard”, Fifth IEEE Southwest Symposium on image Analysis and Interpretation (SSIAI’02), 2002
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[31]The JASPER Project Homepage http://www.ece.uvic.ca/~mdadams/jasper/
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Chapter 6 Classification Techniques of Remote Sensing Image Data
[34]Xiuping Jia, J.A. Richards, “Cluster-space representation for hyperspectral data classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, Issue 3 , pp.593 – 598, March 2002
[35]Xiuping Jia, “Block-based maximum likelihood classification for hyperspectral remote sensing data”, Geoscience and Remote Sensing, 1997. IGARSS ''97. ''Remote Sensing - A Scientific Vision for Sustainable Development''., 1997 IEEE International , Vol. 2 , 3-8, pp.778 – 780 vol.2, Aug 1997
[36]Fuan Tsai, W.D. Philpot, “A derivative-aided hyperspectral image analysis system for land-cover classification”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, Issue 2 , pp.416 – 425, Feb. 2002
[37]S. Kaewpijit, J. Le Moigne, T. El-Ghazawi, “ Automatic reduction of hyperspectral imagery using wavelet spectral analysis”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, Issue 4, pp.863 – 871, April 2003
[38]G. Vane et al., “The airborne visible/infrared imaging spectrometer (AVIRIS)”, Remote Sensing Environment, Vol. 44, no. 2/3, pp. 127 – 143, May/June 1993

Chapter 7 Spectral Unmixing and Target Detection
[39]N. Keshava, J. F. Mustard, “Spectral Unmixing”, IEEE Signal Processing Magazine, Vol. 19, Issue 1, pp. 44 – 57 , Jan. 2002
[40]Q. Du, S. Chakrarvarty, “Unsupervised hyperspectral image classification using blind source separation”, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ''03). Vol. 3 , 6-10, pp. III – 437 – 40 vol.3, April 2003
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[45]Te-Ming Tu; Chin-Hsing Chen; Chein-I Chang, “A noise subspace projection approach to target signature detection and extraction in an unknown background for hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, Issue 1, pp.171 – 181, Jan. 1998
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[48]R. Roger, J. Arnold, “Reversible image compression bounded by noise”, IEEE Transaction on Geoscience and Remote Sensing, Vo;. 32, pp. 19 – 24, Jan. 1994
[49]Te-Won Lee, M.S. Lewicki, “Unsupervised image classification, segmentation, and enhancement using ICA mixture models”, IEEE Transactions on Image Processing, Vol. 11, Issue 3, pp.270 – 279, March 2002
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