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研究生:吳冠霖
研究生(外文):Kuan-Lin Wu
論文名稱:利用經驗解模法於高光譜資料之降維與光譜解析
論文名稱(外文):Use of the Empirical Mode Decomposition for Dimensionality Reduction and Spectral Unmixing of Hyperspectral Data
指導教授:謝璧妃
指導教授(外文):Pi-Fuei Hsieh
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:61
中文關鍵詞:解析經驗解模法高光譜特徵萃取降維
外文關鍵詞:PCAICAremote sensingdimensionality reductionunmixingempirical mode decompositionfeature extractionhyperspectral
相關次數:
  • 被引用被引用:2
  • 點閱點閱:196
  • 評分評分:
  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:1
  本研究中,『經驗解模法』用來做為高頻譜資料之降維及光譜解析。在遙測環境下,由於人力與交通的關係,取得的資料樣本數通常極其有限,這使得高頻譜資料分類的效能受限於資料的維度。為了消除這樣的限制,一些特徵萃取的方法被提出來,希望能在不失類別分離度的條件下,達到降維的效果。為了萃取相關的特徵,這些方法大部分需仰賴由原維度估量出之統計特性。然而,一旦訓練樣本數相對於資料維度顯示不夠時,估量出之統計特性將不夠準確,影響適當特徵之取得。因此非參數估量一途是值得去探求的。
  本研究提出的方法,基於『經驗解模法』所建構出一個較低維度的空間,以利統計特性的估量。本研究同時發現在不同的模式間存在著群集性,除此之外,『經驗解模法』也被應用於解析『未知來源區分』的問題。於是本研究試著將『經驗解模法』應用於與『未知來源區分』類似的『光譜解析』問題。
  實際施測對象採用一農作區之高頻譜影像資料,內容主要是頻譜相似度大之農作物,分類難度高。實驗的結果得知,在高維度樣本數不足的情況下,我們提出的方法能有效地找出一個低維度的空間來做進一步的特徵萃取供分類之用,達到有效降維的目的。另一方面,透過『經驗解模法』,本研究推演出一個方法取得圖元成分之分佈。與其他方法相較,本研究提出的方法效果頗佳。
  In this study, the empirical mode decomposition (EMD) has shown its use in diverse applications for hyperspectral data analysis. Two issues have been concerned: dimensionality reduction and spectral unmixing.
  In remote sensing, the number of labeled samples is usually limited, which makes the classification performance to be cursed by data dimensionality. To dispel the curse, some feature extraction techniques have been developed to reduce data dimensionality without loss of class separability. However, most of these techniques require accurately estimated class statistics in the full dimensional space in order to extract relevant features. Once the number of training samples is not large enough compared to data dimensionality, the estimates of statistics may not be adequately accurate for extracting appropriate features. It would be wise to perform a parameter-irrelevant processing to reduce dimensionality preliminarily for better estimated statistics.
  A method is proposed in this study based on the empirical mode decomposition to construct a low dimensional space spanned by intrinsic mode functions (IMFs) for better estimation of class statistics. We have also discovered that IMFs are distributed in clusters. The empirical mode decomposition has recently been investigated in the blind source separation problem. Since it is analogous to spectral unmixing, we have applied the empirical mode decomposition for spectral unmixing.
  The proposed approaches are tested on hyperspectral data containing various agricultural crops that could hardly be discriminated. Our experimental results demonstrate the feasibility of the EMD for preliminary dimensionality reduction and a potential use for spectral unmixing analysis. In dimensionality reduction, the proposed approach has given a relatively promising performance, compared to other methods such as the principal component analysis, the projection pursuit, and the wavelet approach. In spectral unmixing problem, the proposed approach has shown its potential in finding constituents of pixels.
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Organization 2
Chapter 2 Empirical Mode Decomposition 3
Chapter 3 Dimensionality Reduction 6
3.1 Introduction 6
3.2 Related Work 8
3.2.1 Principal component analysis (PCA) 9
3.2.2 Projection pursuit (PP) 9
3.2.3 Wavelet transform 10
3.3 EMD for Dimensionality Reduction 12
3.4 Experiments 16
3.4.1 Effect of the dimensionality reduction on feature extraction 18
3.4.2 Effect of training sample size 20
3.4.3 Effect of whitening transform 24
3.4.4 Comparison with other approaches 25
3.5 Discussion 29
Chapter 4 Spectral Unmixing Analysis 30
4.1 Introduction 30
4.1.1 The Linear Mixing Model 31
4.1.2 Algorithms for Linear Unmixing 32
4.2 Endmember Extraction Techniques 35
4.2.1 PPI 35
4.2.2 N-FINDR 35
4.2.3 IEA 36
4.2.4 CCA 37
4.2.5 ORASIS 39
4.2.6 AMEE 40
4.3 Independent Component Analysis 43
4.3.1 Linear mixture model for ICA 43
4.3.2 Simple statements of ICA procedure 44
4.4 EMD and Intrinsic Components Analysis 46
4.4.1 Linear mixture model for EMD analysis 46
4.4.2 Statements of the proposed approach 46
4.5 Experiments 48
4.5.1 Approach through EMD analysis 48
4.5.2 Approach based on ICA algorithm 53
4.6 Discussion 55
Chapter 5 Conclusions 57
Chapter 6 Future Work 59
Reference 60
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