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系統識別號 U0026-0812200910453971
論文名稱(中文) 小波轉換應用於高光譜影像光譜特徵萃取之研究
論文名稱(英文) Spectral Feature Extraction of Hyperspectral Images using Wavelet Transform
校院名稱 成功大學
系所名稱(中) 測量工程學系碩博士班
系所名稱(英) Department of Geomatics
學年度 91
學期 2
出版年 92
研究生(中文) 徐百輝
研究生(英文) Pai-Hui Hsu
學號 p6885101
學位類別 博士
語文別 英文
論文頁數 146頁
口試委員 口試委員-王蜀嘉
口試委員-陳哲俊
指導教授-宮鵬
指導教授-曾義星
口試委員-蔡展榮
口試委員-史天元
中文關鍵字 高光譜影像  小波轉換  特徵萃取 
英文關鍵字 Hyperspectral Images  Feature Extraction  Wavelet Transform 
學科別分類
中文摘要 成像光譜儀所擷取的高光譜影像具有豐富且細緻的地物光譜資訊,此特性對於提升地物辨識能力及土地使用分類精度將有所助益。然而若以傳統的統計分類方法對高光譜影像進行分類時,其高維度的特性要求必須有大量的訓練樣本數目,同時大批的資料量亦降低了計算效率,分類精度也沒有顯著地提升。主要的問題在於當統計方法應用於高維度資料時所產生的「維度詛咒」現象。為了解決這個問題,本研究著重在光譜資料維度的降低,特徵萃取即為降低資料維度的一種方式,其基本觀念為去除較不重要的光譜資訊,只保留有用的特徵以降低高光譜資料的維度。本研究主要提出數種以小波理論為基礎的特徵萃取方法,以獲得對高光譜影像分類有用之光譜特徵。小波轉換為新一代的數學分析工具,其多層解析度及時不變的特性,使得其具有偵測局部訊號結構的能力。首先,我們先對高光譜影像進行離散小波或小波包轉換以獲得一組小波係數,之後再根據所設計的判斷法則選出有利於分類的特徵。因為特徵選取時係在維度較低的子空間中進行,因此有限樣本數目及「維度詛咒」等問題皆可以獲得有效的解決。此外,高光譜影像所提供的吸收帶特徵與光譜反射量等資訊亦可與地物的物理及化學特性產生關連,以往的光譜吸收帶特徵係由專家以人工方式確定,因此本研究亦提出以連續小波轉換自動偵測光譜吸收特徵的方法。由於小波轉換的多尺度特性,我們可以很精確地確定光譜吸收特徵之局部結構。當每一個單獨的吸收帶特徵位置被確定之後,即可以連續統消除演算法去除其他的外在效應,如背景吸收帶所造成的輻射量變化及光譜曲線的傾斜。進一步我們定義並計算描述單一吸收帶特徵之參數,未來將可作為光譜比對或地物辨識之用。本研究以兩組高光譜資料來測試所提小波轉換應用於光譜特徵萃取之有效性。實驗結果顯示以小波理論所萃取出的光譜特徵確實可以有效降低高光譜影像的資料維度,同時保持影像分類之精度;此外連續小波方法亦可以有效地萃取出大氣吸收帶特徵的位置。
英文摘要 The rich and detailed spectral information provided by hyperspectral images can be used to identify and quantify a large range of surface materials which cannot be identified by multispectral images. However, the classification methods that have been successfully applied to multispectral data in the past are not as effective as for hyperspectral data. The main problem is that the training data set does not increase corresponding to the increase of dimensionality of hyperspectral data. Actually, the problem of the “curse of dimensionality” emerges when a statistical classification method applied to hyperspectral data. A simpler, but sometimes very effective way of dealing with hyperspectral data is to reduce the number of dimensionality. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited set of training samples. The goal of feature extraction is to reduce the number of dimensionality substantially without sacrificing useful information. In this study, several methods based on the wavelet transforms are developed to extract useful features for classification. Firstly, wavelet or wavelet packet transforms are implemented on the hyperspectral images and a sequence of wavelet coefficients is produced. Then, a simple feature selection procedure associated with a criterion is used to select the effective features for classification. Because the wavelet-based feature extraction optimizes the criterion in a lower dimensional space, the problems of limited training sample size and the curse of dimensionality can be avoided. On the other hand, the laboratory-like spectral curves also describe diagnostic absorption and reflection features which are valuable to understand the physical or chemical properties of materials. The other objective of this study is to propose an automatic procedure for the detection of absorption features. By the zooming procedure of continuous wavelet transform (CWT), these localized structures of absorption features can be accurately characterized. While the position of an individual absorption feature is determined, the continuum removal algorithm can be implemented to reduce some additive effects, such as level changes and slopes due to other absorbing or emitting materials. Consequently the parameters of a single absorption feature can be calculated for spectral matching or material identification. Finally, two AVIRIS data sets are used to test the performance of the proposed wavelet-based methods. The experiment results show that the wavelet-based methods perform well for dimensionality reduction and also be effective for classification.
論文目次 中 文 摘 要 I
ABSTRSCT II
ACKNOWLEDGEMENTS III
CONTENTS IV
LIST OF FIGURES VII
LIST OF TABLES X

CHAPTER 1 INTRODUCTION - 1 -
1.1 IMAGING SPECTROMETERS - 1 -
1.2 MOTIVATIONS - 4 -
1.3 RESEARCH OBJECTIVES - 6 -
1.4 THESIS ORGANIZATION - 7 -

CHAPTER 2 PROPERTIES OF HYPERSPECTRAL IMAGES - 9 -
2.1 INTRODUCTION - 9 -
2.2 CHEMICAL AND PHYSICAL PROPERTIES - 10 -
2.2.1 Absorption Bands - 10 -
2.2.2 Atmospheric Correction - 14 -
2.2.3 Summary - 17 -
2.3 STATISTICAL AND GEOMETRICAL PROPERTIES - 18 -
2.3.1 Spectral Signatures and Distributions - 18 -
2.3.2 Basic Statistics of Distributions - 21 -
2.3.3 Geometric Properties in High-Dimensional Space - 23 -
2.3.4 Summary - 26 -
2.4 CURSE OF DIMENSIONALITY - 27 -
2.4.1 Statistical Pattern Recognition - 27 -
2.4.2 Curse of Dimensionality - 28 -
2.4.3 Summary - 30 -
2.5 VISUALIZATION OF HYPERSPECTRAL IMAGES - 31 -
2.5.1 Introduction - 31 -
2.5.2 Spatial-Spectral Space - 32 -
2.5.3 Spectral Space - 33 -
2.5.4 Scatter Plots - 36 -
2.5.5 Parallel Coordinates - 39 -
2.5.6 Statistics Images - 41 -
2.5.7 Symbolic Representation - 43 -

CHAPTER 3 DIMENSIONALITY REDUCTION OF HYPERSPECTERAL IMAGE - 45 -
3.1 INTRODUCTION - 45 -
3.2 FEATURE SELECTION - 46 -
3.2.1 Criterion Function - 47 -
3.2.2 Optimal Search Procedure - 48 -
3.2.3 Suboptimal Search Procedure - 51 -
3.2.4 Experiments - 53 -
3.3 FEATURE EXTRACTION - 54 -
3.3.1 The Definition of Features - 55 -
3.3.2 Ideal Features for Classification - 56 -
3.4 FEATURE EXTRACTION ALGORITHM OVERVIEW - 57 -
3.4.1 Principal Components Analysis - 58 -
3.4.2 Linear Discriminant Analysis - 59 -
3.4.3 Decision Boundary Feature Extraction Algorithm - 62 -
3.5 EXPERIMENTS - 63 -
3.5.1 Experiment I - 63 -
3.5.2 Experiment II - 67 -
3.6 SUMMARY - 72 -

CHAPTER 4 WAVELET-BASED FEATURE EXTRACTION - 73 -
4.1 INTRODUCTION - 73 -
4.2 FOURIER-BASED FEATURE EXTRACTION - 74 -
4.3 WAVELET-BASED FEATURE EXTRACTION - 77 -
4.3.1 Wavelet Transform - 77 -
4.3.2 Fast Orthogonal Wavelet Transform - 80 -
4.3.3 Linear Wavelet Feature Extraction - 82 -
4.3.4 Experiment I - 88 -
4.3.5 Non-linear Wavelet Feature Extraction - 91 -
4.3.6 Best-Basis Feature Extraction - 94 -
4.3.7 Local Discriminant Basis Feature Extraction - 100 -
4.4 EXPERIMENTS - 103 -
4.4.1 Experiment I - 103 -
4.4.2 Experiment II - 112 -
4.5 SUMMARY - 114 -

CHAPTER 5 ABSOPRTION FEATURE DETECTION OF HYPERSPECTRAL IMAGES - 116 -
5.1 INTRODUCTION - 116 -
5.2 ABSORPTION FEATURE EXTRACTION - 117 -
5.2.1 Definition of Absorption Features - 117 -
5.2.2 Continuum Removal of Absorption Features - 118 -
5.2.3 Summary of Methods for Absorption Features Extraction - 120 -
5.2.4 Fingerprints - 121 -
5.3 WAVELET-BASED FEATURE EXTRACTION - 123 -
5.3.1 Continuous Wavelet Transform - 123 -
5.3.2 Absorption Feature Detection using Modulus Maxima Wavelet Transform - 124 -
5.4 EXPERIMENTS - 133 -
5.4.1 Chlorophyll Absorption Detection - 133 -
5.4.2 Absorption Information Image - 135 -
5.5 SUMMARY - 136 -

CHAPTER 6 CONCLUSION - 138 -
6.1 Summary - 138 -
6.2 SuggestIONs for Further Work - 140 -
BIBLIOGRAPHY - 142 -
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