Title page for 88342012


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Student Number 88342012
Author Yi-chen Shao(ªò©É¸Û)
Author's Email Address shaoyc@cc.chit.edu.tw
Statistics This thesis had been viewed 2634 times. Download 749 times.
Department Civil Engineering
Year 2006
Semester 2
Degree Ph.D.
Type of Document Doctoral Dissertation
Language English
Title Ground Point Selection and Building Detection from Airborne LiDAR Data
Date of Defense 2007-06-29
Page Count 155
Keyword
  • airboren LiDAR
  • building detection
  • classification
  • DEM
  • laser scanning
  • Abstract In the dissertation, ground points are selected and building regions are detected from airborne Light Detection and Ranging (LiDAR) data. In the first part of the paper the system, error sources, and the data features of the airborne LiDAR are described after which several major filtering algorithms and their characteristics are reviewed and compared. A novel slope-based climbing-and-sliding (CAS) method is developed to select ground points from airborne LIDAR data which takes into consideration the features of height, slope and area of the region of bare earth. In the proposed method not only is a local search performed but the merits of a global treatment are preserved. A scheme is proposed to improve the efficiency and accuracy where the ground points in the initial surface model are selected based on a novel pseudo-grid. After this a back selection step is performed to retrieve detailed terrain features. Considering that bridges have tended to be misclassified as parts of the ground, an additional detection step is included to remove bridge points.
    In the second part of the dissertation, building regions in the set of above-ground points are detected. Prior knowledge of such things as the height, size, and area of the buildings, is employed to first remove extraneous points or regions and to detect building candidates. Building and tree are two main dominate classes in the candidates. Based on the assumption that buildings roofs are piece-wise continuous, ten region-based textures based on directional slope difference are analyzed. At the last, an unsupervised classification is performed for the building detection.
    The filtering error of the generated DEM is evaluated, as well as the test of parameter sensitivity. The processing results are quantitatively compared with several recognized counterparts in the literature. The presentation of the terrain features is also analyzed. The experimental results demonstrate that the proposed scheme can be applied to diverse terrain types. To validate the detection scheme, two data sets including urban and rural areas in Taiwan are tested. The experimental results indicate that two features of homogeneity and probability of a small slope difference preserve most information and thus are most suitable for the detection.
    Table of Content ºK­ni
    ABSTRACTii
    »xÁÂiv
    List of Figuresviii
    List of Acronymsxii
    1.Introduction1
    1-1 Backgrounds1
    1-2 Definition of Terms4
    1-3 Research Methods and Objectives8
    1-4 Contribution of this Study9
    1-5 Outline of the Dissertation10
    2.Related Work on Terrain Reconstruction and Building Detection from Airborne LiDAR Data12
    2-1 Airborne LiDAR Data12
    2-1-1 Airborne LiDAR System12
    2-1-2 Sources of ALS Data Error16
    2-1-3 Features of ALS Data18
    2-2 Related Works on Filtering22
    2-2-1 Surface-based Concept26
    2-2-2 Region-based Concept27
    2-2-3 Slope-based Concept29
    2-2-4 Discussion of Filtering Methods30
    2-3 Related Works on Building Detection34
    2-3-1 Methods Developed for Building Detection34
    2-3-2 Discussion of Building Detection Methods39
    3.Ground Point Selection from Airborne LiDAR Data40
    3-1 Proposed Methods for Ground Point Selection40
    3-1-1 CAS Method40
    3-1-1-1 Concept behind the CAS Method41
    3-1-1-2 CAS Process43
    3-1-2 Bridge Detection44
    3-2 Implementation of Ground Point Selection46
    3-2-1 Generation of Initial Surface Model49
    3-2-2 Noise Removal51
    3-2-3 Searching for Ground Points53
    3-2-4 Bridge Removal57
    3-2-5 Back Selection of Ground Points60
    4.Building Detection from Airborne LiDAR Data64
    4-1 Proposed Method for Building Detection64
    4-1-1 Prior Knowledge65
    4-1-2 Directional Slope Difference65
    4-1-3 Computation of Region-Based Texture68
    4-2 Implementation of Building Detection72
    4-2-1 Generation of nDSM74
    4-2-2 Pre-process to Detect Building Candidates75
    4-2-3 Building Detection76
    5.Experimental Results79
    5-1 Definition of Errors79
    5-2 Validation of Ground Point Selection81
    5-2-1 Testing with ISPRS Data82
    5-2-2 Testing with Taiwan Data93
    5-3 Validation of Building Detection100
    5-3-1 Pre-processing Results for Building Candidates102
    5-3-2 Detection Results by the Classification of Region Textures106
    5-4 Summary112
    5-4-1 Advantages and Limitations of Ground Point Selection113
    5-4-2 Advantages and Limitations of Building Detection115
    6.Conclusion and Future Work117
    6.1 Conclusion117
    6.2 Future work119
    References121
    Appendix A: ISPRS Data Sets130
    A-1 Characteristics of the ISPRS filter test data130
    A-2 Shaded relief maps of the ISPRS test sites131
    A-3 Shaded relief maps of the DSM and DEM of test Samples135
    Appendix B: Taiwan Data Sets and the Generated DEM140
    B-1 Characteristics of Taiwan Data Sets140
    B-2 Shaded relief maps of the Taiwan test sites141
    B-3 Shaded relief maps of the DSM and DEM of test Samples143
    Appendix C: Classification Results of the Test Data147
    C-1 Characteristics of the test data for building detection147
    C-2 Classification results and the error analysis for test Area A149
    C-3 Classification results and the error analysis for test Area B152
    Curriculum Vitae155
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    Advisor
  • Liang-chien Chen(³¯¨}°·)
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    Date of Submission 2007-07-21

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