1. NCCU Academic Hub
  2. 學術產出
  3. 社會科學學院
  4. 學位論文
  5. Item

學術產出-學位論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 旋翼UAS影像密匹配建物點雲自動分群之研究
Automatic clustering of building point clouds from dense matching VTOL UAS images
作者 林柔安
Lin, Jou An
貢獻者 邱式鴻
Chio, Shih Hong
林柔安
Lin, Jou An
關鍵詞 影像密匹配
無人機系統
Mean shift分群法
dense image matching
Unmanned Aerial System (UAS)
mean shift clustering
日期 2015
上傳時間 1-十月-2015 14:23:04 (UTC+8)
摘要 三維城市模型之建置需求漸趨繁多，可提供都市規劃、城市導航及虛擬實境等相關應用，過去研究多以建置LOD2城市模型為主，且較著重於屋頂結構。近年來，逐漸利用垂直影像及傾斜影像作為原始資料，提供建物牆面之建置，並且，隨著無人機系統(Unmanned Aircraft System, UAS）發展，可利用其蒐集高解析度且高重疊垂直及傾斜拍攝之建物影像，並採影像密匹配技術產製高密度點雲，進而快速取得建物包含屋頂及牆面之三維資訊，而這些資訊可進一步提供後續建置LOD3建置層級之模型，而在建置前，首先須對資料進行特徵分析，萃取特徵點、線、面，進而提供建置模型所需之資訊。
因此，本研究期望利用密匹配點雲，計算其點雲特徵，並採用Mean Shift分群法（Comaniciu and Meer, 2002）萃取建物點雲資訊，並提供一最佳分群策略。首先，本研究將以UAS為載具，設計一野外率定場率定相機，並蒐集建物高重疊UAS影像密匹配產製高密度點雲，針對單棟建物高密度點雲，實驗測試點雲疏化程度後，依據疏化成果計算點雲特徵，並以此批點雲資料實驗測試Mean shift分群法（Cheng, 1995）中之參數，後設計分群流程以分離平面點群及曲面點群，探討分群成果以決定最佳分群策略。實驗結果顯示本研究提出之分群策略，可自動區分平面點群及曲面點群，並單獨將平面點群分群至各牆面。
Unmanned Aerial System (UAS) offer several new possibilities in a wide range of applications. One example is the 3D reconstruction of buildings. In former times this was either restricted by earthbound vehicles to the reconstruction of facades or by air-borne sensors to generate only very coarse building models. UAS are able to observe the whole 3D scene and to capture images of the object of interest from completely different perspectives.
Therefore, this study will use UAS to collected images of buildings and to generate point cloud from dense image matching for modeling buildings. In the proposed approach, this method computes principal orientations by PCA and identifies clusters by Mean shift clustering. Analyze the factors which can affect the clustering methods and try to decrease the use of threshold, and this result can cluster the façade of buildings automatically and offer the after building reconstruction for LOD3.
參考文獻 王正忠，2002，「以近景攝影測量進行模型式建物重建」，成功大學測量工程學系學位論文：台南。
王淼，2011，「光達點雲區塊化」，國立成功大學測量及空間資訊學系博士論文：台南。
中華民國航空測量及遙感探測學會，2014，「建立航遙測感應器系統校正作業4年總報告(100至103年)」，內政部國土測繪中心委託工作總報告。
王蜀嘉、曾義星，2003，「高精度及高解析度數值地形模型測製規範」，內政部委託工作報告。
王聖鐸，2005，「以浮測模型理論萃取三維空間資訊-以建物重建為例」，『航測及遙測學刊』，12(4)：489-507。
李孟儒，2011，「利用近景影像提高三維建物模型之細化等級」，國立中央大學土木工程學系碩士論文：桃園。
沈柏琦，2007，「利用中值平移分類法作點雲之模型重建」，國立成功大學測量及空間資訊學系碩士論文：台南。
李硯婷，2013，「空照影像密匹配之效能與品質」，論文發表於〈測量及空間資訊研討會〉，國立交通大學：新竹，民國102年8月29日至30日。
林耿帆，2012，「以物件為基礎之光達點雲分類」，國立台灣大學土木工程學系碩士論文：台北。
洪祥恩，2011，「以地面及空載光達點雲重建複雜物三維模型」，國立中央大學土木系碩士論文：桃園。
施凱倫，2014，「利用測繪車影像萃取道路標誌重建細部道路模型」，國立中央大學土木系碩士論文：桃園。
陳英煥，2007，「空照數位像機拍攝高重疊影像匹配高密度點雲」，國立成功大學測量及空間資訊學系碩士論文：台南。
高崇軒，2011，「以多重疊近景影像萃取牆面三維線段之研究」，交通大學土木工程系所學位論文：新竹。
黃世涵，2012，「以最小二乘平面套合法進行空載與車載光達點雲套合」，交通大學土木工程系所學位論文：新竹。
曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯，2006，『資料探勘』，台北：旗標出版股份有限公司。
鄭源松，2009，「改良式平均移動法於多目標影像追蹤之即時嵌入化實現」，交通大學電機與控制工程研究所學位論文：新竹。
蔡依庭，2012，「UAV 航拍影像點雲產生DSM之研究」，臺北大學不動產與城鄉環境學系學位論文：台北。
劉嘉銘，2005，「光達點雲資料特徵萃取之研究」，成功大學測量及空間資訊學系學位論文：台南。
賴泓瑞，2009，「以模型樣版為基礎之建物三維點雲建模演算法」，成功大學測量及空間資訊學系學位論文：台南。
藍裕翔，2014，「航照影像特徵輔助之半全域匹配於數值地表模型建立」，國立中央大學土木系碩士論文：桃園。
Becker, S. and Haala, N., 2007, “Refinement of Building Fassades by Integrated Processing of LIDAR and Image Data”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, 36: 7-12.
Bertram, T. T., Bockb, T. T., Bulgakovc, A. G. and Evgenovd, A. A., 2014, “Generation the 3D Model Building by Using the Quadcopter”, Paper presented at the 31st, International Symposium on Automation and Robotics in Construction and Mining, Sydney, Australia, July 9-11.
Böhm, J. and Haala, N., 2005, “Efficient Integration of Aerial and Terrestrial Laser Data for Virtual City Modeling Using Lasermaps”, Paper presented at the ISPRS Workshop Laser Scanning 2005, Enschede, the Netherlands, September 12-14.
Bulatov, D., Häufel, G., Meidow, J., Pohl, M., Solbrig, P. and Wernerus, P., 2014, “Context-Based Automatic Reconstruction and Texturing of 3D Urban Terrain for Quick-Response Tasks”, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 157-170.
Comaniciu, D. and Meer, P., 2002, “Mean Shift: A Robust Approach toward Feature Space Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5): 603-619.
Denis, E. and Baillard, C ., 2010, “Refining Existing 3D Building Models with Terrestrial Laser Points Acquired from a Mobile Mapping Vehicle”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38: 195-200.
Eisenbeiss, H., 2004, “A Mini Unmanned Aerial Vehicle (UAV): System Overview and Image Acquisition”, Paper presented at the International Workshop on Processing and Visualization using High-Resolution Imagery, Pitsanulok, Thailand, November 18-20.
Ester, M., Kriegel, H. P., Sander, J. and Xu, X., 1996, "A density-based algorithm for discovering clusters in large spatial databases with noise", Paper presented at the International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 2-4.
Fischler, M. A. and Bolles, R. C., 1981, “Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, 24(6): 381-395.
Förstner, W. and Gülch, E., 1987, “A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features”, Paper presented at the ISPRS Intercommission Workshop, Interlaken, Switzerland, June 2-4.
Fritsch, D., Becker, S. and Rothermel, M., 2013, “Modeling Façade Structures using Point Clouds from Dense Image Matching”, Paper presented at the International Conference on Advances in Civil, Structural and Mechanical Engineering, HongKong, August 3-4.
Furukawa, Y. and Ponce, J., 2010, “Accurate, Dense, and Robust Multiview Stereopsis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8): 1362-1376.
Gerke, M. and Xiao, J., 2014, “Fusion of Airborne Laserscanning Point Clouds and Images for Supervised and Unsupervised Scene Classification”, ISPRS Journal of Photogrammetry and Remote Sensing, 87: 78-92.
Golub, G. H. and Van Loan, C. F., 1980, “An Analysis of the Total Least Squares Problem”, SIAM Journal on Numerical Analysis, 17(6): 883-893.
Ho, H. T., 2009, 3D Surface Matching from Range Images using Multiscale Local Features, Unpublished doctoral dissertation, The University of Adelaide, Australia.
Joshi, R., 2014, Automated 3D feature extraction for simple geometry buildings using images for GIS data collection, Unpublished doctoral dissertation, University of Twente, Enschede, the Netherlands.
Kazhdan, M., Bolitho, M. and Hoppe, H., 2006, “Poisson Surface Reconstruction”, Paper presented at the fourth Eurographics Symposium on Geometry Processing, Cagliari, Sardinia, Italy, June 26-28.
Kwak, E., 2013, Automatic 3D Building Model Generation by Integrating Lidar and Aerial Images using a Hybrid Approach, Unpublished doctoral dissertation, University of Calgary, Canada.
Lowe, D. G., 2004, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2): 91-110.
Mayer, H., 1999, “Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings”, Computer Vision and Image Understanding, 74(2): 138-149.
Nex, F., Gerke, M., Remondino, F., Przybilla, H., Bäumker, M. and Zurhorst, A., 2015, “ISPRS Benchmark for Multi-Platform Photogrammetry”, Paper presented at the PIA15+HRIGI15 – Joint ISPRS conference, Munich, Germany, March 25-27.
Petrie, G. and Toth, C. K., 2009, “Terrestrial Laser Scanners.” pp. 87-128 in Topographic Laser Ranging and Scanning Principles and Processing, edit by Shan J. and Toth C. K., Boca Raton: CRC Press.
Previtali, M., Barazzetti, L., Brumana, R., Cuca, B., Oreni, D., Roncoroni, F. and Scaioni, M., 2014, “Automatic Façade Modelling using Point Cloud Data for Energy-Efficient Retrofitting”, Applied Geomatics, 6(2): 95-113.
Qin, R. and Gruen, A., 2014, “3D Change Detection at Street Level using Mobile Laser Scanning Point Clouds and Terrestrial Images”, ISPRS Journal of Photogrammetry and Remote Sensing, 90: 23-35.
Rau, J. Y., Jhan, J. P. and Hsu, Y. C., 2015, “Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment”, Geoscience and Remote Sensing, 53(3): 1304-1319.
Remondino, F., Barazzetti, L., Nex, F., Scaioni, M. and Sarazzi, D., 2011, “UAV Photogrammetry for Mapping and 3D Modeling - Current Status and Future Perspectives”, Paper presented at the ISPRS Zurich 2011 Workshop, Zurich, Switzerland, September 14-16.
Rothermel, M., Haala, N., Wenzel, K. and Bulatov, D., 2014, “Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams”, Paper presented at the 22nd International Conference Pattern Recognition, Stockholm, August 24-28.
Shahzad, M. and Zhu, X. X., 2015, “Robust Reconstruction of Building Facades for Large Areas using Spaceborne TomoSAR Point Clouds”, Geoscience and Remote Sensing, 53(2): 752-769.
Tarsha-Kurdi, F., Landes, T. and Grussenmeyer, P., 2008, “Extended RANSAC Algorithm for Automatic Detection of Building Roof Planes from LiDAR Data”, The Photogrammetric Journal of Finland, 21(1): 97-109.
Vetrivel, A., Gerke, M., Kerle, N. and Vosselman, G., 2015a, “Identification of Damage in Buildings Based on Gaps in 3D Point Clouds from Very High Resolution Oblique Airborne Images”, ISPRS Journal of Photogrammetry and Remote Sensing, 105: 61-78.
Vetrivel, A., Gerke, M., Kerle, N. and Vosselman, G., 2015b, “Segmentation of UAV-Based Images Incorporating 3D Point Cloud Information”, Paper presented at the PIA15+HRIGI15 – Joint ISPRS conference, Munich, Germany, March 25-27.
Vosselman, G., Gorte, B. G., Sithole, G. and Rabbani, T., 2004, “Recognising Structure in Laser Scanner Point Clouds”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 46: 33-38.
Vosselman, G. and Maas, H. G., 2010, Airborne and Terrestrial Laser Scanning, Boca Raton: CRC Press.
Wefelscheid, C., Hänsch, R. and Hellwich, O., 2011, “Three-Dimensional Building Reconstruction using Images Obtained by Unmanned Aerial Vehicles”, Paper presented at the ISPRS Zurich 2011 Workshop, Zurich, Switzerland, September 14-16.
Weinmann, M., Urban, S., Hinz, S., Jutzi, B. and Mallet, C., 2015, “Distinctive 2D and 3D Features for Automated Large-Scale Scene Analysis in Urban Areas”, Computers and Graphics, 49: 47-57.
Xiong, B., Elberink, S. O. and Vosselman, G., 2014a, “Building Modeling from Noisy Photogrammetric Point Clouds”, Paper presented at the ISPRS Technical Commission III Symposium, Zurich, Switzerland, September 5-7.
Xiong, B., Elberink, S. O. and Vosselman, G., 2014b, “A Graph Edit Dictionary for Correcting Errors in Roof Topology Graphs Reconstructed from Point Clouds”, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 227-242.
Xiong, B., Jancosek, M., Elberink, S. O. and Vosselman, G., 2015, “Flexible Building Primitives for 3D Building Modeling”, ISPRS Journal of Photogrammetry and Remote Sensing, 101: 275-290.
Xu, S., Vosselman, G. and Elberink, S. O., 2014, “Multiple-Entity Based Classification of Airborne Laser Scanning Data in Urban Areas”, ISPRS Journal of Photogrammetry and Remote Sensing, 88: 1-15.
Zhang, J., Duan, M., Yan, Q. and Lin, X., 2014, “Automatic Vehicle Extraction from Airborne Lidar Data using an Object-Based Point Cloud Analysis Method”, Remote Sensing, 6(9): 8405-8423.
USGS(2008). Digital Aerial Imagery Calibration Range Requirements Version 0.2, EROS Remote Sensing Technology Project. Retrieved August 28, 2009 from USGS on the World Wide Web:
http://calval.cr.usgs.gov/digital_aerial_imaging_quality_assurance.php
描述 碩士
國立政治大學
地政研究所
102257030
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102257030
資料類型 thesis
dc.contributor.advisor 邱式鴻zh_TW
dc.contributor.advisor Chio, Shih Hongen_US
dc.contributor.author (作者) 林柔安zh_TW
dc.contributor.author (作者) Lin, Jou Anen_US
dc.creator (作者) 林柔安zh_TW
dc.creator (作者) Lin, Jou Anen_US
dc.date (日期) 2015en_US
dc.date.accessioned 1-十月-2015 14:23:04 (UTC+8)-
dc.date.available 1-十月-2015 14:23:04 (UTC+8)-
dc.date.issued (上傳時間) 1-十月-2015 14:23:04 (UTC+8)-
dc.identifier (其他 識別碼) G0102257030en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/78777-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 地政研究所zh_TW
dc.description (描述) 102257030zh_TW
dc.description.abstract (摘要) 三維城市模型之建置需求漸趨繁多，可提供都市規劃、城市導航及虛擬實境等相關應用，過去研究多以建置LOD2城市模型為主，且較著重於屋頂結構。近年來，逐漸利用垂直影像及傾斜影像作為原始資料，提供建物牆面之建置，並且，隨著無人機系統(Unmanned Aircraft System, UAS）發展，可利用其蒐集高解析度且高重疊垂直及傾斜拍攝之建物影像，並採影像密匹配技術產製高密度點雲，進而快速取得建物包含屋頂及牆面之三維資訊，而這些資訊可進一步提供後續建置LOD3建置層級之模型，而在建置前，首先須對資料進行特徵分析，萃取特徵點、線、面，進而提供建置模型所需之資訊。
因此，本研究期望利用密匹配點雲，計算其點雲特徵，並採用Mean Shift分群法（Comaniciu and Meer, 2002）萃取建物點雲資訊，並提供一最佳分群策略。首先，本研究將以UAS為載具，設計一野外率定場率定相機，並蒐集建物高重疊UAS影像密匹配產製高密度點雲，針對單棟建物高密度點雲，實驗測試點雲疏化程度後，依據疏化成果計算點雲特徵，並以此批點雲資料實驗測試Mean shift分群法（Cheng, 1995）中之參數，後設計分群流程以分離平面點群及曲面點群，探討分群成果以決定最佳分群策略。實驗結果顯示本研究提出之分群策略，可自動區分平面點群及曲面點群，並單獨將平面點群分群至各牆面。		zh_TW
dc.description.abstract (摘要) Unmanned Aerial System (UAS) offer several new possibilities in a wide range of applications. One example is the 3D reconstruction of buildings. In former times this was either restricted by earthbound vehicles to the reconstruction of facades or by air-borne sensors to generate only very coarse building models. UAS are able to observe the whole 3D scene and to capture images of the object of interest from completely different perspectives.
Therefore, this study will use UAS to collected images of buildings and to generate point cloud from dense image matching for modeling buildings. In the proposed approach, this method computes principal orientations by PCA and identifies clusters by Mean shift clustering. Analyze the factors which can affect the clustering methods and try to decrease the use of threshold, and this result can cluster the façade of buildings automatically and offer the after building reconstruction for LOD3.		en_US
dc.description.tableofcontents 謝誌 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
第一節 研究動機與目的 1
第二節 論文架構 4
第二章 文獻回顧 6
第一節 建物模型重建方法 6
一、 建物重建之策略面 6
二、 建物重建之資料面 8
第二節 點雲處理 11
一、 光達點雲處理 11
二、 密匹配點雲處理 15
第三節 分群法 17
一、 群集分析 18
二、 分群法於建物重建方面之應用 20
第四節 小結 22
第三章 研究方法與理論基礎 24
第一節 點雲產製 26
一、 相機率定 26
二、 影像密匹配 29
第二節 點雲前處理 29
一、 點雲疏化 30
二、 點雲特徵萃取 30
第三節 點雲分群 33
一、 Mean shift分群法 33
二、 分群參數 35
三、 最佳分群策略之探討 35
第四章 實驗成果與分析 38
第一節 點雲產製 38
一、 相機率定 38
二、 影像密匹配 40
第二節 影響分群之因素 43
一、 點雲疏化 45
二、 分群參數之探討 50
三、 最佳分群策略之探討 59
第三節 最佳分群策略之分群成果 69
第五章 結語與建議 78
參考文獻 80		zh_TW
dc.format.extent 6678592 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102257030en_US
dc.subject (關鍵詞) 影像密匹配zh_TW
dc.subject (關鍵詞) 無人機系統zh_TW
dc.subject (關鍵詞) Mean shift分群法zh_TW
dc.subject (關鍵詞) dense image matchingen_US
dc.subject (關鍵詞) Unmanned Aerial System (UAS)en_US
dc.subject (關鍵詞) mean shift clusteringen_US
dc.title (題名) 旋翼UAS影像密匹配建物點雲自動分群之研究zh_TW
dc.title (題名) Automatic clustering of building point clouds from dense matching VTOL UAS imagesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 王正忠，2002，「以近景攝影測量進行模型式建物重建」，成功大學測量工程學系學位論文：台南。
王淼，2011，「光達點雲區塊化」，國立成功大學測量及空間資訊學系博士論文：台南。
中華民國航空測量及遙感探測學會，2014，「建立航遙測感應器系統校正作業4年總報告(100至103年)」，內政部國土測繪中心委託工作總報告。
王蜀嘉、曾義星，2003，「高精度及高解析度數值地形模型測製規範」，內政部委託工作報告。
王聖鐸，2005，「以浮測模型理論萃取三維空間資訊-以建物重建為例」，『航測及遙測學刊』，12(4)：489-507。
李孟儒，2011，「利用近景影像提高三維建物模型之細化等級」，國立中央大學土木工程學系碩士論文：桃園。
沈柏琦，2007，「利用中值平移分類法作點雲之模型重建」，國立成功大學測量及空間資訊學系碩士論文：台南。
李硯婷，2013，「空照影像密匹配之效能與品質」，論文發表於〈測量及空間資訊研討會〉，國立交通大學：新竹，民國102年8月29日至30日。
林耿帆，2012，「以物件為基礎之光達點雲分類」，國立台灣大學土木工程學系碩士論文：台北。
洪祥恩，2011，「以地面及空載光達點雲重建複雜物三維模型」，國立中央大學土木系碩士論文：桃園。
施凱倫，2014，「利用測繪車影像萃取道路標誌重建細部道路模型」，國立中央大學土木系碩士論文：桃園。
陳英煥，2007，「空照數位像機拍攝高重疊影像匹配高密度點雲」，國立成功大學測量及空間資訊學系碩士論文：台南。
高崇軒，2011，「以多重疊近景影像萃取牆面三維線段之研究」，交通大學土木工程系所學位論文：新竹。
黃世涵，2012，「以最小二乘平面套合法進行空載與車載光達點雲套合」，交通大學土木工程系所學位論文：新竹。
曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯，2006，『資料探勘』，台北：旗標出版股份有限公司。
鄭源松，2009，「改良式平均移動法於多目標影像追蹤之即時嵌入化實現」，交通大學電機與控制工程研究所學位論文：新竹。
蔡依庭，2012，「UAV 航拍影像點雲產生DSM之研究」，臺北大學不動產與城鄉環境學系學位論文：台北。
劉嘉銘，2005，「光達點雲資料特徵萃取之研究」，成功大學測量及空間資訊學系學位論文：台南。
賴泓瑞，2009，「以模型樣版為基礎之建物三維點雲建模演算法」，成功大學測量及空間資訊學系學位論文：台南。
藍裕翔，2014，「航照影像特徵輔助之半全域匹配於數值地表模型建立」，國立中央大學土木系碩士論文：桃園。
Becker, S. and Haala, N., 2007, “Refinement of Building Fassades by Integrated Processing of LIDAR and Image Data”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, 36: 7-12.
Bertram, T. T., Bockb, T. T., Bulgakovc, A. G. and Evgenovd, A. A., 2014, “Generation the 3D Model Building by Using the Quadcopter”, Paper presented at the 31st, International Symposium on Automation and Robotics in Construction and Mining, Sydney, Australia, July 9-11.
Böhm, J. and Haala, N., 2005, “Efficient Integration of Aerial and Terrestrial Laser Data for Virtual City Modeling Using Lasermaps”, Paper presented at the ISPRS Workshop Laser Scanning 2005, Enschede, the Netherlands, September 12-14.
Bulatov, D., Häufel, G., Meidow, J., Pohl, M., Solbrig, P. and Wernerus, P., 2014, “Context-Based Automatic Reconstruction and Texturing of 3D Urban Terrain for Quick-Response Tasks”, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 157-170.
Comaniciu, D. and Meer, P., 2002, “Mean Shift: A Robust Approach toward Feature Space Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5): 603-619.
Denis, E. and Baillard, C ., 2010, “Refining Existing 3D Building Models with Terrestrial Laser Points Acquired from a Mobile Mapping Vehicle”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38: 195-200.
Eisenbeiss, H., 2004, “A Mini Unmanned Aerial Vehicle (UAV): System Overview and Image Acquisition”, Paper presented at the International Workshop on Processing and Visualization using High-Resolution Imagery, Pitsanulok, Thailand, November 18-20.
Ester, M., Kriegel, H. P., Sander, J. and Xu, X., 1996, "A density-based algorithm for discovering clusters in large spatial databases with noise", Paper presented at the International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 2-4.
Fischler, M. A. and Bolles, R. C., 1981, “Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, 24(6): 381-395.
Förstner, W. and Gülch, E., 1987, “A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features”, Paper presented at the ISPRS Intercommission Workshop, Interlaken, Switzerland, June 2-4.
Fritsch, D., Becker, S. and Rothermel, M., 2013, “Modeling Façade Structures using Point Clouds from Dense Image Matching”, Paper presented at the International Conference on Advances in Civil, Structural and Mechanical Engineering, HongKong, August 3-4.
Furukawa, Y. and Ponce, J., 2010, “Accurate, Dense, and Robust Multiview Stereopsis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8): 1362-1376.
Gerke, M. and Xiao, J., 2014, “Fusion of Airborne Laserscanning Point Clouds and Images for Supervised and Unsupervised Scene Classification”, ISPRS Journal of Photogrammetry and Remote Sensing, 87: 78-92.
Golub, G. H. and Van Loan, C. F., 1980, “An Analysis of the Total Least Squares Problem”, SIAM Journal on Numerical Analysis, 17(6): 883-893.
Ho, H. T., 2009, 3D Surface Matching from Range Images using Multiscale Local Features, Unpublished doctoral dissertation, The University of Adelaide, Australia.
Joshi, R., 2014, Automated 3D feature extraction for simple geometry buildings using images for GIS data collection, Unpublished doctoral dissertation, University of Twente, Enschede, the Netherlands.
Kazhdan, M., Bolitho, M. and Hoppe, H., 2006, “Poisson Surface Reconstruction”, Paper presented at the fourth Eurographics Symposium on Geometry Processing, Cagliari, Sardinia, Italy, June 26-28.
Kwak, E., 2013, Automatic 3D Building Model Generation by Integrating Lidar and Aerial Images using a Hybrid Approach, Unpublished doctoral dissertation, University of Calgary, Canada.
Lowe, D. G., 2004, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2): 91-110.
Mayer, H., 1999, “Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings”, Computer Vision and Image Understanding, 74(2): 138-149.
Nex, F., Gerke, M., Remondino, F., Przybilla, H., Bäumker, M. and Zurhorst, A., 2015, “ISPRS Benchmark for Multi-Platform Photogrammetry”, Paper presented at the PIA15+HRIGI15 – Joint ISPRS conference, Munich, Germany, March 25-27.
Petrie, G. and Toth, C. K., 2009, “Terrestrial Laser Scanners.” pp. 87-128 in Topographic Laser Ranging and Scanning Principles and Processing, edit by Shan J. and Toth C. K., Boca Raton: CRC Press.
Previtali, M., Barazzetti, L., Brumana, R., Cuca, B., Oreni, D., Roncoroni, F. and Scaioni, M., 2014, “Automatic Façade Modelling using Point Cloud Data for Energy-Efficient Retrofitting”, Applied Geomatics, 6(2): 95-113.
Qin, R. and Gruen, A., 2014, “3D Change Detection at Street Level using Mobile Laser Scanning Point Clouds and Terrestrial Images”, ISPRS Journal of Photogrammetry and Remote Sensing, 90: 23-35.
Rau, J. Y., Jhan, J. P. and Hsu, Y. C., 2015, “Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment”, Geoscience and Remote Sensing, 53(3): 1304-1319.
Remondino, F., Barazzetti, L., Nex, F., Scaioni, M. and Sarazzi, D., 2011, “UAV Photogrammetry for Mapping and 3D Modeling - Current Status and Future Perspectives”, Paper presented at the ISPRS Zurich 2011 Workshop, Zurich, Switzerland, September 14-16.
Rothermel, M., Haala, N., Wenzel, K. and Bulatov, D., 2014, “Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams”, Paper presented at the 22nd International Conference Pattern Recognition, Stockholm, August 24-28.
Shahzad, M. and Zhu, X. X., 2015, “Robust Reconstruction of Building Facades for Large Areas using Spaceborne TomoSAR Point Clouds”, Geoscience and Remote Sensing, 53(2): 752-769.
Tarsha-Kurdi, F., Landes, T. and Grussenmeyer, P., 2008, “Extended RANSAC Algorithm for Automatic Detection of Building Roof Planes from LiDAR Data”, The Photogrammetric Journal of Finland, 21(1): 97-109.
Vetrivel, A., Gerke, M., Kerle, N. and Vosselman, G., 2015a, “Identification of Damage in Buildings Based on Gaps in 3D Point Clouds from Very High Resolution Oblique Airborne Images”, ISPRS Journal of Photogrammetry and Remote Sensing, 105: 61-78.
Vetrivel, A., Gerke, M., Kerle, N. and Vosselman, G., 2015b, “Segmentation of UAV-Based Images Incorporating 3D Point Cloud Information”, Paper presented at the PIA15+HRIGI15 – Joint ISPRS conference, Munich, Germany, March 25-27.
Vosselman, G., Gorte, B. G., Sithole, G. and Rabbani, T., 2004, “Recognising Structure in Laser Scanner Point Clouds”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 46: 33-38.
Vosselman, G. and Maas, H. G., 2010, Airborne and Terrestrial Laser Scanning, Boca Raton: CRC Press.
Wefelscheid, C., Hänsch, R. and Hellwich, O., 2011, “Three-Dimensional Building Reconstruction using Images Obtained by Unmanned Aerial Vehicles”, Paper presented at the ISPRS Zurich 2011 Workshop, Zurich, Switzerland, September 14-16.
Weinmann, M., Urban, S., Hinz, S., Jutzi, B. and Mallet, C., 2015, “Distinctive 2D and 3D Features for Automated Large-Scale Scene Analysis in Urban Areas”, Computers and Graphics, 49: 47-57.
Xiong, B., Elberink, S. O. and Vosselman, G., 2014a, “Building Modeling from Noisy Photogrammetric Point Clouds”, Paper presented at the ISPRS Technical Commission III Symposium, Zurich, Switzerland, September 5-7.
Xiong, B., Elberink, S. O. and Vosselman, G., 2014b, “A Graph Edit Dictionary for Correcting Errors in Roof Topology Graphs Reconstructed from Point Clouds”, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 227-242.
Xiong, B., Jancosek, M., Elberink, S. O. and Vosselman, G., 2015, “Flexible Building Primitives for 3D Building Modeling”, ISPRS Journal of Photogrammetry and Remote Sensing, 101: 275-290.
Xu, S., Vosselman, G. and Elberink, S. O., 2014, “Multiple-Entity Based Classification of Airborne Laser Scanning Data in Urban Areas”, ISPRS Journal of Photogrammetry and Remote Sensing, 88: 1-15.
Zhang, J., Duan, M., Yan, Q. and Lin, X., 2014, “Automatic Vehicle Extraction from Airborne Lidar Data using an Object-Based Point Cloud Analysis Method”, Remote Sensing, 6(9): 8405-8423.
USGS(2008). Digital Aerial Imagery Calibration Range Requirements Version 0.2, EROS Remote Sensing Technology Project. Retrieved August 28, 2009 from USGS on the World Wide Web:
http://calval.cr.usgs.gov/digital_aerial_imaging_quality_assurance.php		zh_TW