XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/7: THEORY AND EXPERIMENTS IN RADAR AND LIDAR]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: TENSOR-BASED QUALITY PREDICTION FOR BUILDING MODEL RECONSTRUCTION FROM LIDAR DATA AND TOPOGRAPHIC MAP B. C. Lin, R. J. You

Document type:: Multivolume work

Structure type:: Chapter

essment Lisbon, s, and EEE | > ange- al of h Side- Model. 7). verla F. terrain. 3301: ding of M data. 968. Image r of 94-300. z ERS SA SP- on. ITC Artech X: A ns., d on :P302- International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia TENSOR-BASED QUALITY PREDICTION FOR BUILDING MODEL RECONSTRUCTION FROM LIDAR DATA AND TOPOGRAPHIC MAP B. C. Lin!*, R. J. You? Department of Geomatics, National Cheng Kung University, 1 University Road, Tainan City, Taiwan — ! cclin0520(g gmail.com *rjyou@mail.ncku.edu.tw KEY WORDS: LiDAR, tensor analysis, robust least squares, data fusion, building model reconstruction ABSTRACT: A quality prediction method is proposed to evaluate the quality of the automatic reconstruction of building models. In this study, LiDAR data and topographic maps are integrated for building model reconstruction. Hence, data registration is a critical step for data fusion. To improve the efficiency of the data fusion, a robust least squares method is applied to register boundary points extracted from LiDAR data and building outlines obtained from topographic maps. After registration, a quality indicator based on the tensor analysis of residuals is derived in order to evaluate the correctness of the automatic building model reconstruction. Finally, an actual dataset demonstrates the quality of the predictions for automatic model reconstruction. The results show that our method can achieve reliable results and save both time and expense on model reconstruction. 1. INTRODUCTION The airborne LiDAR technique has been extensively adopted for the purpose of quickly acquiring a large number of highly qualitative point clouds, and it has become widely implemented in 3D building models. LiDAR data provides an accurate representation of building surfaces, but since it has poor texture information, accurate building boundary extraction from LiDAR data may be difficult to obtain (Maas and Vosselman 1999). Therefore, the building boundaries can be implemented by using additional data sources, such as 2D topographic maps. Since a data fusion of LiDAR data and 2D map information takes advantage of both surface and boundary information, a great many researchers have investigated it in order to reconstruct buildings (Maas and Vosselman 1999; Vosselman and Dijkman 2001; Filin 2002; Overby et al. 2004). In general, building roof patch features are first extracted from LiDAR data. Next, building models are reconstructed by combining the building boundaries obtained from ground plans and the intersection lines of adjacent planar faces derived from LiDAR data. However, the coordinate systems of LiDAR data and 2D maps are often different. To overcome the problem of coordinate systems of various data sources, data registration is a critical step for fusion of LiDAR data and the topographic map information (Schenk and Csatho 2002; Filin et al. 2005; Gruen and Akca 2005; Park et al. 2006). In this study, plane segments in LiDAR data are extracted in the feature space based on the tensor voting computational framework (Medioni et al. 2000). The tensor voting algorithm implements features such as faces, lines and points through a symmetric tensor field directly derived from data. All geometric structures (surfaces, lines and points) can therefore be inferred simultaneously. * Corresponding author. For data registration, the transformation parameters between LiDAR data and topographic maps are estimated using a robust least squares method (RLS). After registration, height information derived from LIDAR data is involved in topographic maps and then the spatial positions of building outlines can be reconstructed. To completely reconstruct a 3D building model, the roof ridges extracted from LiDAR data should be added (You and Lin 2011a). To achieve reliable results, a quality indicator derived from tensor analysis based on the residuals of the boundary point is introduced. The indicator can be use for checking the correctness of the building model in an automatic reconstruction process. Therefore, both time and expense on model reconstruction can be saved. In the following, the feature extraction based on the tensor voting method is first described. In section 3, the data registration and residual tensor analysis are address. Finally, the residual tensor on different building cases of actual airborne LiDAR dataset is analyzed. 2. FEATURE EXTRACTION BASED ON TENSOR VOTING METHOD In this study, the tensor voting method (TVM) is adopted to extract roof faces from LiDAR data since this method can sufficiently consider the geometric relationships between surrounding points. In the TVM, a second order tensor field should be first constituted, and then planar features, namely roof faces, can be extracted from irregularly distributed LIDAR point clouds (You and Lin 2011a). The geometric feature of a point can be described by a second- order symmetric tensor which is expressed as follows:

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