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系統識別號 U0026-2201201508491700
論文名稱(中文) 空載光達波形資料之響應偵測與土地覆蓋分類
論文名稱(英文) Echo Detection and Land Cover Classification of Airborne Waveform LiDAR Data
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 103
學期 1
出版年 104
研究生(中文) 王正楷
研究生(英文) Cheng-Kai Wang
學號 P68961027
學位類別 博士
語文別 英文
論文頁數 112頁
口試委員 指導教授-曾義星
口試委員-陳良健
口試委員-史天元
口試委員-趙鍵哲
口試委員-王驥魁
中文關鍵字 空載光達  波形  響應  偵測器  小波  土地覆蓋分類 
英文關鍵字 Airborne  Waveform LiDAR  Echo  Detector  Wavelet  Land cover classification 
學科別分類
中文摘要 當前發展的空載光達波形系統,比傳統離散光達系統能夠提供更多額外的資訊。波形資料所記錄的訊息,大致可分成地物的空間位置和物理特性兩種,地物的空間位置乃透過偵測波形中之地物反射訊號,再藉由已知雷射光當時發射的方向和位置資訊,進而推測出地物之空間位置,此偵測波形中地物反射訊號的方法,稱為響應偵測;而地物的反射系數或表面粗糙度等物理特性,對於雷射光也會有不同的反應,這些反應會形成其特有的波形特徵,即可作為地物分類之依據。因此,對於波形資訊的萃取,通常會先偵測出地物在波形中之位置,再萃取地物之波形特徵,作進一步的分析或分類之用。對於響應偵測,傳統離散光達系統在執行掃描時,即同時進行,此方式容易遺失微弱響應或重疊之響應,導致在某些具有多重回波效應的場景裡(如森林區),點雲的幾何描述能力不足,降低後續產品的品質和精度,如數值高程模型的製作。本研究針對此問題,提出一個基於小波轉換的方法,利用其具有偵測微弱響應和解析重疊響應之能力,來重新找回這些遺失的點雲,進而增進數值高程模型的自動化處理和產品精度;本研究首先使用模擬波形資料來測試小波偵測器和其他兩種常用的偵測器:過零值法和高斯分解法,比較它們對於微弱和重疊響應偵測之能力。實驗的結果顯示,三種偵測器透過模擬資料的比較,小波偵測器和高斯分解法對於重疊響應有明顯優於過零值法的趨勢,而小波偵測器在當兩重疊小波皆有較低的訊雜比時,有最佳的結果。接著將小波偵測器應用在森林區真實掃描的波形資料進行點雲偵測,其結果顯示小波偵測器可額外增加31.5%的點數,實驗中亦透過視覺和統計的方式來證實小波偵測器能夠找出微弱和重疊響應之點群。為了進一步了解重新找回這些點雲對於數值高程模型製作的提升程度,我們透過一個自動化的點雲過濾程序將這些萃取後的點雲進行地面點與非地面點之分類,實驗結果顯示,由於從波形資料所獲取的點雲其幾何描述能力較佳,因此過濾的分類精度和數值地面模型之精度都有所提升。對於波形特徵之應用,本研究將其用來作為土地覆蓋物分類之研究,而波形特徵在本研究中乃分為二類,一類為基於響應特徵,另一類為基於波形特徵。目前相關的研究通常使用基於響應特徵來進行,而在本研究中,也發現此類特徵對於單回波的波形分類有很好的效果,然而當波形具有多重回波時,基於響應的特徵往往會無法區分某些地物類別,因此本研究則進一步的使用基於波形的特徵來彌補此基於響應特徵之缺點。實驗的成果顯示,當整合這兩種特徵時,土地覆蓋分類的成果會比傳統單獨使用基於響應特徵的方法,在某些具有多重回波的地物類別上,獲得明顯之改善。
英文摘要 Compared with discrete LiDAR systems, state-of-the-art airborne waveform LiDAR systems provide richer information on illuminated surfaces. Waveform data contains both the spatial and physical information of the surfaces. The geospatial surfaces can be located by detecting the reflected laser signal stored in the waveform with the information of the laser travelling path. The process to detect the reflected signal is known as echo detection. The physical characteristics of surfaces such as the reflectance or surface roughness will deform the shape of the transmitting laser pulse resulting in different waveform features. Such features can be used for land cover classification. For waveform information extraction, the echoes are usually detected before the waveform features are extracted for further analysis. For echo detection, conventional discrete LiDAR systems often use an on-the-fly process to detect points. This process usually misdetects weak or overlapping echoes, thus resulting in poor geometry when the structure of a scanned area is complex, such as a forest area. This study proposes an echo detection approach based on wavelet transformation that is capable of detecting weak returns and resolving overlapping echoes. Simulated and real waveform datasets of a forest area were both used in this study. The simulated waveform data were utilized to compare the proposed detector with two other popular detectors, namely, zero crossing (ZC) and Gaussian decomposition (GD), in terms of their ability to deal with weak or overlapping echoes. The real waveform dataset were used to demonstrate the wavelet-based (WB) algorithm for exploring missing echoes. Experiments using simulated data showed that the WB and GD detectors are superior to the ZC detector in finding overlapping echoes. The WB algorithm performs well when dealing with overlapping echoes with low signal-to-noise ratio. The proposed WB algorithm was then applied to the real waveform dataset to test its effectiveness in detecting missing echoes. Results show that the WB algorithm can find more than 31.5% number of points than that of the used LiDAR system. An automatic filtering process was applied to the point clouds extracted from the waveform data to classify the ground points. This paper presents assessment methods based on the visual analyses of point classification and on the elevation difference of generated digital elevation models. Results show that the filtering accuracy and the accuracy of the digital elevation model are both improved because an enhanced geometry of the landscape can be obtained from the detected points. For land cover classification, features that can be derived from waveform data to describe land covers are divided into two categories, namely, echo-based and waveform-based features. Echo-based features have been widely used by previous studies to effectively classify land covers when the waveform has a single return. When the waveform contains multi-returns, echo-based features would fail to distinguish some land covers. Thus, waveform-based features are used and investigated in this study to complement the disadvantages of echo-based features. Experiments show that land cover classification can be improved with the integration of echo-based and waveform-based features.
論文目次 摘 要 I
ABSTRACT II
ACKNOWLEDGEMENTS IV
CONTENTS V
LIST OF TABLES VIII
LIST OF FIGURES IX
LIST OF ACRONYMS XIV
Chapter 1: Introduction 1
1.1 Background 1
1.1.1 Digital elevation model and land covers 1
1.1.2 Airborne LiDAR remote sensing 2
1.2 Research motivation 3
1.2.1 Discrete-return airborne LiDAR system 3
1.2.2 Full-waveform airborne LiDAR system 4
1.3 Research objective 5
1.3.1 Proposition of a wavelet-based detector 5
1.3.2 The use of points detected from waveform data for DEM generation 6
1.3.3 The use of waveform features for land cover classification 7
1.3.4 Summary 8
1.4 Dissertation structure 9
CHAPTER 2: TOPOGRAPHIC AIRBORNE WAVEFORM LIDAR SYSTEM 10
2.1 Laser 10
2.2 Principle of laser ranging 11
2.3 Laser scanning 12
2.4 Principle of point positioning 14
2.5 Waveform LiDAR system 16
2.5.1 Digitization of waveform 17
2.5.2 Waveform modeling 18
2.5.3 Waveform simulation 20
2.5.4 Influences of target properties on waveforms 21
2.6 Summary 29
CHAPTER 3: WAVELET-BASED ECHO DETECTOR 30
3.1 Introduction 30
3.2 Review of echo detectors 31
3.2.1 Zero-crossing method 31
3.2.2 Gaussian decomposition algorithm 32
3.3 Wavelet-based echo detector 33
3.4 Noise estimation 37
3.5 Procedure of detecting echoes 38
3.6 SNR of echoes 40
3.7 Test of the weak echo detection 40
3.8 Test of overlapping echo detection 41
3.9 Experimental results and discussions 44
3.9.1 Test Results of Weak Echo Detection 44
3.9.2 Test Results of Overlapping Echo Detection 45
3.10 Summary 49
CHAPTER 4: WAVEFORM POINT CLOUD EXTRACTION AND FILTERING 50
4.1 Introduction 50
4.2 Test area and real waveform data 51
4.3 Point cloud extraction from waveform data 55
4.4 Point cloud filtering 55
4.5 EXPERIMENTS 57
4.5.1 Results of ground point filtering 57
4.5.2 Ground point density on DEM accuracy 66
CHAPTER 5: WAVEFORM CLASSIFICATION FOR LAND COVER IDENTIFICATION 71
5.1 Introduction 71
5.2 Methodology 72
5.3 Waveform decomposition 74
5.4 Feature generation 75
5.4.1 Echo-based features 75
5.4.2 Waveform-based features 76
5.5 Experiments and results 79
5.5.1 Study area and the land cover classes 79
5.5.2 Feature analysis 81
5.5.3 Classification accuracy 91
CHAPTER 6: CONCLUSION 103
6.1 Concluding remarks 103
6.2 Research contribution and originality 105
6.3 Research limitations and assumptions 105
6.4 Recommendations for future work 106
BIBLIOGRAHY 108
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