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研究生: 吳昱瑱
Wu, Yu-Tien
論文名稱: 探討適用於地面光達資料之半經驗式雙向反射分布函數模型
Evaluation of Semi-empirical Bidirectional Reflectance Distribution Function Models for Terrestrial Laser Scanning Data
指導教授: 王驥魁
Wang, Chi-Kuei
學位類別: 碩士
Master
系所名稱: 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 77
中文關鍵詞: 地面光達雙向反射分布函數RPV模型Kernel-driven模型
外文關鍵詞: Terrestrial Laser Scanning, Bidirectional Reflectance Distribution Function, Rahman–Pinty–Verstraete Model, Kernel-driven Models
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  • 地面光達之強度值可被用來描述物體表面之相對反射率,且雷射光束之入射角、雷射發射位置與物體表面間之距離、及物體表面種類皆會影響地面光達之強度值。雙向反射分布函數可用來描述入射角及地面光達強度值之間的關係,本研究採用五種遙測之半經驗式雙向反射分布函數模型來探討其描述地面光達強度值資料在不同入射角之特性,其中包含有RPV模型及四種kernel-driven模型,總共使用十二種人造表面來測試各模型的適用性,並依據視覺檢視擬合之整體趨勢及決定係數來評估各半經驗式雙向反射分布函數模型對地面光達強度值擬合曲線之擬合好壞。結果顯示Rossthin-Roujean模型及RPV模型最適用於地面光達資料。

    Terrestrial Laser Scanning (TLS) intensity data can be regarded as the relative reflectance of the surface material. TLS intensity is a function of the angle of incidence of the laser beam, the range distance between the laser and the surface, and the surface type. The Bidirectional Reflectance Distribution Function (BRDF) was used to describe the relation between the angle of incidence and TLS intensity. Five remote sensing semi-empirical BRDF models, including Rahman–Pinty–Verstraete (RPV) model and four kernel-driven models derived from Ross (1981), were evaluated using twelve flat sample materials. The goodness of fit of the BRDF models to the TLS intensity are evaluated based on visual examination of the fitted trend and their corresponding coefficients of determination. The results show that the Rossthin-Roujean model and RPV model are the most suitable for TLS data.

    ABSTRACT i 中文摘要 ii 誌謝 iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES xii Chapter 1 Introduction 1 Chapter 2 Method 9 2.1 Range correction 9 2.2 Plane feature detection of the TLS points 11 2.3 Bidirectional Reflectance Distribution Function (BRDF) Models 13 2.3.1 The Rahman–Pinty–Verstraete (RPV) model 13 2.3.2 Kernel-driven Models 16 2.3.3 Semi-empirical models applied to the LiDAR condition 31 2.3.4 Derivation of BRDF model parameters 41 Chapter 3 Material 42 3.1 Experiment setup and data acquisition 42 Chapter 4 Results and discussion 46 4.1 Semi-empirical BRDF model fitting results 46 4.1.1 RPV model results 57 4.1.2 Kernel-driven models results 62 4.2 Pure mathematical function fitting results 65 Chapter 5 Conclusions 70 REFERENCE 72

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