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研究生:黃郁閔
研究生(外文):Yu-Min Huang
論文名稱:基於凸分析之超光譜影像分離演算法
論文名稱(外文):Convex Analysis Based Unmixing Algorithm for Hyperspectral Imaging
指導教授:祁忠勇馬榮健
指導教授(外文):Chong-Yung ChiWing-Kin Ma
學位類別:碩士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
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:2008
畢業學年度:96
語文別:英文
論文頁數:39
中文關鍵詞:凸分析超光譜影像分離演算法
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Hyperspectral imaging techniques have been developed for a wide range of remote sensing applications in both civilian and military, including terrain classification, environmental monitoring, agricultural monitoring, geological exploration, and military surveillance. A common problem in hyperspectral imaging is that a large part of pixels contain more than one type of spectral signatures (or endmembers). The hyperspectal unmixing problem aims at identifying the hidden endmembers and their corresponding proportions (or abundances) from an observed hyperspectral scene. In planetary exploration, hyperspectral unmixing provides a powerful tool for analyzing the composition and mineralogy of the observed planetary surfaces. In this thesis, we propose a hyperspectral unmixing algorithm using convex analysis. The algorithm, called the minimum simplex volume algorithm (MSVA), considers a challenging case where no pure pixel is assumed to be present. It is an alternating minimization approach for hyperspectral image unmixing using a minimum simplex volume criterion. We provide a hyperspectral unmixing formulation where the goal is to find a ‘best’ data-enclosing simplex by minimizing the simplex volume. We then propose a novel cyclic minimization procedure that uses linear programs (LPs) to sequentially reduce the simplex volume. The MVSA is based on solving LPs, and hence it can be efficiently implemented by using readily available LP solvers. And the proposed algorithm is capable of obtaining endmembers and fractional abundances simultaneously. Some Monde Carlo simulations and real data experiments are presented to demonstrate the efficacy of the proposed method over several existing unmixing methods.
CONTENTS
CHINESE ABSTRACT i
ABSTRACT iii
ACKNOWLEDGMENTS iv
CONTENTS v
1 INTRODUCTION 1
2 PROBLEM STATEMENT AND ASSUMPTIONS 5
3 SOME BASIC CONCEPTS OF CONVEX ANALYSIS 8
3.1 Affine Hull 8
3.2 Convex Hull 10
4 HYPERSPECTRAL UNMIXING USING MINIMUM SIMPLEX VOLUME
CRITERION 11
4.1 Unmixing Problem by Minimum Simplex Volume Criterion 12
4.2 Minimum Simplex Volume Algorithm (MSVA) 16
5 COMPUTER SIMULATIONS 20
5.1 Performance Evaluation for The Noiseless Case 21
5.2 Performance Evaluation for Finite SNR 24
6 APPLICATION TO SYNTHETIC IMAGE 26
7 REAL HYPERSPECTRAL IMAGE EXPERIMENTS 30
8 CONCLUSION 35
REFERENCES 36
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