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題名 利用近紅外光影像之近景攝影測量建立數值表面模型之研究
Construction of digital surface model using Near-IR close range photogrammetry作者 廖振廷
Liao, Chen Ting貢獻者 黃灝雄
Huang, Hao Hsiung
廖振廷
Liao, Chen Ting關鍵詞 近紅外光影像
近景攝影測量
點雲
數值表面模型
Near Infrared Images
Close Range Photogrammetry
Point Cloud
Digital Surface Model (DSM)日期 2011 上傳時間 3-Dec-2012 11:28:23 (UTC+8) 摘要 點雲（point cloud）為以大量三維坐標描述地表實際情形的資料形式，其中包含其三維坐標及相關屬性。通常點雲資料取得方式為光達測量，其以單一波段雷射光束掃描獲取資料，以光達獲取點雲，常面臨掃描時間差、缺乏多波段資訊、可靠邊緣線及角點資訊、大量離散點雲又缺乏語意資訊（semantic information）難以直接判讀及缺乏多餘觀測量等問題。攝影測量藉由感測反射自太陽光或地物本身放射之能量，可記錄為二維多光譜影像，透過地物在不同光譜範圍表現之特性，可輔助分類，改善分類成果。若匹配多張高重疊率的多波段影像，可以獲取包含多波段資訊且位於明顯特徵點上的點雲，提供光達以外的點雲資料來源。傳統空中三角測量平差解算地物點坐標及產製數值表面模型（Digital Surface Model, DSM）時，多採用可見光影像為主；而目前常見之高空間解析度數值航照影像，除了記錄可見光波段之外，亦可蒐集近紅外光波段影像。但較少採用近紅外光波段影像，以求解地物點坐標及建立DSM。因此本研究利用多波段影像所蘊含的豐富光譜資訊，以取像方式簡易及低限制條件的近景攝影測量方式，匹配多張可見光、近紅外光及紅外彩色影像，分別建立可見光、近紅外光及紅外彩色之DSM，其目的在於探討加入近紅外光波段後，所產生的近紅外光及紅外彩色DSM，和可見光DSM之異同；並比較該DSM是否更能突顯植被區。研究顯示，以可見光點雲為檢核資料，計算近紅外光與紅外彩色點雲的均方根誤差為其距離門檻值之相對檢核方法，可獲得約21%的點雲增加率；然而使用近紅外光或紅外彩色影像，即使能增加點雲資料量，但對於增加可見光影像未能匹配的資料方面，其效果仍屬有限。
Point cloud represents the surface as mass 3D coordinates and attributes. Generally, these data are usually collected by LIDAR (LIght Detection And Ranging), which acquires data through single band laser scanning. But the data collected by LIDAR could face problems, such as scanning process is not instantaneous, lack of multispectral information, breaklines, corners, semantic information and redundancies.However, photogrammetry record the electromagnetic energy reflected or emitted from the surface as 2D multispectral images, via ground features with different characteristics differ in spectrum, it can be classified more efficiently and precisely. By matching multiple high overlapping multispectral images, point cloud including multispectral information and locating on obvious feature points can be acquired. This provides another point cloud source aparting from LIDAR.In most studies, visible light (VIS) images are used primarily, while calculating ground point coordinates and generating digital surface models （DSM） through aerotriangulation. Although nowadays, high spatial resolution digital aerial images can acquire not only VIS channel, but also near infrared (NIR) channel as well. But there is lack of research doing the former procedures by using NIR images.Therefore, this research focuses on the rich spectral information in multispectral images, by using easy image collection and low restriction close range photogrammetry method. It matches several VIS, NIR and color infrared (CIR) images, and generate DSMs respectively. The purpose is to analyze the difference between VIS, NIR and CIR data sets, and whether it can emphasize the vegetation area, after adding NIR channel in DSM generation.The result shows that by using relative check points between NIR, CIR data with VIS one. First, VIS point cloud was set as check point data, then, the RMSE (Root Mean Square Error) of NIR and CIR point cloud was calculated as distance threshold. Its data increment is 21% ca. However, the point cloud data amount can be increased, by matching NIR and CIR images. But the effect of increasing data, which was not being matched from VIS images are limited.參考文獻 一、中文參考文獻王淼、湯凱佩、曾義星，2005，「光達資料八分樹結構化於平面特徵萃取」，『航測及遙測學刊』，10(1)：59-70。王蜀嘉、張祖勛，2006，「航測數位像機對空載雷射掃描帶來的衝擊」，〈第二十五屆測量及空間資訊研討會〉，清雲科技大學：桃園，民國95年9月7日至8日。周啟鳴、劉學軍，2006，『數字地形分析』第一版，北京：科學出版社。洪祥恩，2011，「以地面及空載光達點雲重建複雜建物三維模型」，國立中央大學土木工程學系碩士論文：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：臺南。張祥儀，2008，「融合光達與影像資料於建物偵測之研究」，國立宜蘭大學土木工程學系研究所碩士論文：宜蘭。黃智遠，2008，「整合形狀及光譜資訊於房屋模型之變遷偵測」，國立中央大學土木工程學系碩士論文：桃園。鄒芳諭，2010，「以非量測性相機進行近景攝影測量探討」，國立交通大學土木工程學系碩士論文：新竹。楊旻憲，2009，「應用光達點雲資料與多譜影像處理技術萃取樹冠特徵資訊」，國立嘉義大學農學院森林暨自然資源研究所碩士論文：嘉義。鄭邦寧，2007，「使用空載光達點雲求定數值地表高程模型之小波法」，國立成功大學測量及空間資訊學系碩士論文：臺南。潘國樑，2009，『遙測學大綱：遙測概念、原理與影像判釋技術』第二版，臺北：科技圖書股份有限公司。蔡建成，2009，「以空載光達同步航空攝影製作高程模型品值探討」，國立交通大學土木工程學系碩士論文：新竹。賴君怡，2008，「整合空載光達資料與多光譜影像建立樹木模型—以成大校園為例」，國立成功大學測量及空間資訊學系碩士論文：臺南。蕭淳伊，2008，「應用空載光達資料與遙測影像推估樹林分布及體積」，國立成功大學測量及空間資訊學系碩士論文：臺南。謝幸宜，2011，「以自率光束法提升四旋翼UAV影像之定位精度」，國立政治大學地政學系碩士論文：臺北。二、外文參考文獻Baltsavias, E. P., 1999, “A comparison between photogrammetry and laser scanning”, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 83-94.Charaniya, A. P., R. Manduchi, and S. K. Lodha, 2004, ”Supervised Parametric Classification of Aerial LiDAR Data”, paper presented at the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Washington, DC, US, June 27-July 2.Detchev, I., A. Habib, and J. Y. Rau, 2011, “Image Matching for 3D Photogrammetric Reconstruction”, paper presented at the 32nd, Asian Conference on Remote Sensing, Taipei, Taiwan, October 3-7.Fritsch, D., A. M. Khosravani, A. Cefalu, and K. Wenzel, 2011, “Multi-Sensors and Multiray Reconstruction for Digital Preservation”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.Henricsson, O., F. Bignone, W. Willuhn, F. Ade, O. Kübler, E. Baltsavias, S. Mason, and A. Grün, 1996, ‚“Project AMOBE: Strategies, Current Status and Future Work”, International Archives of Photogrammetry and Remote Sensing, paper presented at the 18th ISPRS Congress, Vienna, Austria, July 9-19. Haala, N., and C. Brenner, 1999, “Extraction of buildings and trees in urban environments”, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 130-137.Hoegner, L., and U. Stilla, 2008, “Case Study of the 5-point Algorithm for Texturing Existing Building Models from Infrared Image Sequences”, International Archives of Photogrammetry, Remote Sensing and Spatial Geoinformation Sciences, 37(B3B): 479-484.Hoegner, L., and U. Stilla, 2009, “Thermal leakage detection on building facades using infrared textures generated by mobile mapping”, paper presented at Joint Urban Remote Sensing Event 2009, Shanghai, China, May 20-22.Jensen, J. R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edition, US: Pearson Education, Inc.Kremer, J., 2011, “Power Line Mapping: Data Acquisition with A Specialized Multi-Sensor Platform”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.Mikhail, E. M., J. S. Bethel, and J. C. McGlone, 2001, Introduction to Modern Photogrammetry, New York: John Wiley & Sons, Inc.McGlone, J. C., E. M. Mikhail, J. S. Bethel, and R. Mullen, 2004, Manual of Photogrammetry, 5th Edition, Maryland: American Society for Photogrammetry and Remote Sensing.Minten, H., 2011, “News from IGI”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.Seager, S., E. L. Turner, J. Schafer, and E. B. Ford, 2005, “Vegetation’s Red Edge: A Possible Spectroscopic Biosignature of Extraterrestrial Plants”, Astrobiology, 5(3): 372-390.TOPCON Positioning Systems Inc., 2008, “Operation Manual”, Tokyo, Japan. TOPCON Positioning Systems Inc.Vosselman, G., and H. G. Maas, 2010, Airborne and Terrestrial Laser Scanning, Scotland, UK: Whittles Publishing.Wolf, P. R., and B. A. Dewitt, 2004, Elements of Photogrammetry With Applications in GIS, 3rd Edition, US: McGraw-Hill.三、網頁參考文獻iWitness (2009). Frequently Asked Questions. Retrieved May 24, 2012 from iWitness on the World Wide Web: http://www.iwitnessphoto.com/iwitness/faqs.htmlPhotometrix (2010, August). Users Manual for iWitness and iWitnessPRO. Retrieved January 9, 2012 from World Wide Web: http://www.photometrix.com.au 描述 碩士
國立政治大學
地政研究所
99257006
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099257006 資料類型 thesis dc.contributor.advisor 黃灝雄 zh_TW dc.contributor.advisor Huang, Hao Hsiung en_US dc.contributor.author (Authors) 廖振廷 zh_TW dc.contributor.author (Authors) Liao, Chen Ting en_US dc.creator (作者) 廖振廷 zh_TW dc.creator (作者) Liao, Chen Ting en_US dc.date (日期) 2011 en_US dc.date.accessioned 3-Dec-2012 11:28:23 (UTC+8) - dc.date.available 3-Dec-2012 11:28:23 (UTC+8) - dc.date.issued (上傳時間) 3-Dec-2012 11:28:23 (UTC+8) - dc.identifier (Other Identifiers) G0099257006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/56336 - 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) 99257006 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 點雲（point cloud）為以大量三維坐標描述地表實際情形的資料形式，其中包含其三維坐標及相關屬性。通常點雲資料取得方式為光達測量，其以單一波段雷射光束掃描獲取資料，以光達獲取點雲，常面臨掃描時間差、缺乏多波段資訊、可靠邊緣線及角點資訊、大量離散點雲又缺乏語意資訊（semantic information）難以直接判讀及缺乏多餘觀測量等問題。攝影測量藉由感測反射自太陽光或地物本身放射之能量，可記錄為二維多光譜影像，透過地物在不同光譜範圍表現之特性，可輔助分類，改善分類成果。若匹配多張高重疊率的多波段影像，可以獲取包含多波段資訊且位於明顯特徵點上的點雲，提供光達以外的點雲資料來源。傳統空中三角測量平差解算地物點坐標及產製數值表面模型（Digital Surface Model, DSM）時，多採用可見光影像為主；而目前常見之高空間解析度數值航照影像，除了記錄可見光波段之外，亦可蒐集近紅外光波段影像。但較少採用近紅外光波段影像，以求解地物點坐標及建立DSM。因此本研究利用多波段影像所蘊含的豐富光譜資訊，以取像方式簡易及低限制條件的近景攝影測量方式，匹配多張可見光、近紅外光及紅外彩色影像，分別建立可見光、近紅外光及紅外彩色之DSM，其目的在於探討加入近紅外光波段後，所產生的近紅外光及紅外彩色DSM，和可見光DSM之異同；並比較該DSM是否更能突顯植被區。研究顯示，以可見光點雲為檢核資料，計算近紅外光與紅外彩色點雲的均方根誤差為其距離門檻值之相對檢核方法，可獲得約21%的點雲增加率；然而使用近紅外光或紅外彩色影像，即使能增加點雲資料量，但對於增加可見光影像未能匹配的資料方面，其效果仍屬有限。 zh_TW dc.description.abstract (摘要) Point cloud represents the surface as mass 3D coordinates and attributes. Generally, these data are usually collected by LIDAR (LIght Detection And Ranging), which acquires data through single band laser scanning. But the data collected by LIDAR could face problems, such as scanning process is not instantaneous, lack of multispectral information, breaklines, corners, semantic information and redundancies.However, photogrammetry record the electromagnetic energy reflected or emitted from the surface as 2D multispectral images, via ground features with different characteristics differ in spectrum, it can be classified more efficiently and precisely. By matching multiple high overlapping multispectral images, point cloud including multispectral information and locating on obvious feature points can be acquired. This provides another point cloud source aparting from LIDAR.In most studies, visible light (VIS) images are used primarily, while calculating ground point coordinates and generating digital surface models （DSM） through aerotriangulation. Although nowadays, high spatial resolution digital aerial images can acquire not only VIS channel, but also near infrared (NIR) channel as well. But there is lack of research doing the former procedures by using NIR images.Therefore, this research focuses on the rich spectral information in multispectral images, by using easy image collection and low restriction close range photogrammetry method. It matches several VIS, NIR and color infrared (CIR) images, and generate DSMs respectively. The purpose is to analyze the difference between VIS, NIR and CIR data sets, and whether it can emphasize the vegetation area, after adding NIR channel in DSM generation.The result shows that by using relative check points between NIR, CIR data with VIS one. First, VIS point cloud was set as check point data, then, the RMSE (Root Mean Square Error) of NIR and CIR point cloud was calculated as distance threshold. Its data increment is 21% ca. However, the point cloud data amount can be increased, by matching NIR and CIR images. But the effect of increasing data, which was not being matched from VIS images are limited. en_US dc.description.tableofcontents 摘要 IAbstract III目錄 V圖目錄 VII表目錄 IX第一章 緒論 1第一節 研究背景 1第二節 研究動機及目的 4一、研究動機 4二、研究目的 6第三節 研究方法及流程 7一、研究方法 7二、研究流程 8第四節 論文架構 9第二章 理論基礎及文獻回顧 11第一節 點雲來源 12一、光達 12二、攝影測量 13三、光達及攝影測量點雲之比較 16第二節 多光譜影像 19一、近紅外光波段 19二、結合點雲和多光譜影像之研究 21第三節 影像匹配 23一、標準化互相關法 24二、最小自乘匹配法 26第四節 數值表面模型 28一、規則網格 29二、不規則三角網 29第五節 小結 31第三章 實驗設計 33第一節 實驗規劃 33一、實驗區 33二、相機率定 36三、資料來源 39第二節 資料處理 41一、近紅外光影像 41二、紅外彩色影像 43第三節 成果檢核方法 44一、絕對精度檢核 44二、相對精度檢核 45第四章 研究成果及分析 47第一節 相機率定成果 47一、率定成果 48二、率定成果探討 50第二節 點雲及DSM產製成果 54一、不同基線距離之像對 55二、可見光點雲及DSM 58三、近紅外光點雲及DSM 62四、紅外彩色點雲及DSM 65五、小結 68第三節 成果檢核及探討 69一、絕對檢核成果 71二、相對檢核成果 78三、建立DSM 83四、小結 87第五章 結論與建議 89第一節 結論 89第二節 建議 92參考文獻 95一、中文參考文獻 95二、外文參考文獻 96三、網頁參考文獻 97附錄 99 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099257006 en_US dc.subject (關鍵詞) 近紅外光影像 zh_TW dc.subject (關鍵詞) 近景攝影測量 zh_TW dc.subject (關鍵詞) 點雲 zh_TW dc.subject (關鍵詞) 數值表面模型 zh_TW dc.subject (關鍵詞) Near Infrared Images en_US dc.subject (關鍵詞) Close Range Photogrammetry en_US dc.subject (關鍵詞) Point Cloud en_US dc.subject (關鍵詞) Digital Surface Model (DSM) en_US dc.title (題名) 利用近紅外光影像之近景攝影測量建立數值表面模型之研究 zh_TW dc.title (題名) Construction of digital surface model using Near-IR close range photogrammetry en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 一、中文參考文獻王淼、湯凱佩、曾義星，2005，「光達資料八分樹結構化於平面特徵萃取」，『航測及遙測學刊』，10(1)：59-70。王蜀嘉、張祖勛，2006，「航測數位像機對空載雷射掃描帶來的衝擊」，〈第二十五屆測量及空間資訊研討會〉，清雲科技大學：桃園，民國95年9月7日至8日。周啟鳴、劉學軍，2006，『數字地形分析』第一版，北京：科學出版社。洪祥恩，2011，「以地面及空載光達點雲重建複雜建物三維模型」，國立中央大學土木工程學系碩士論文：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：臺南。張祥儀，2008，「融合光達與影像資料於建物偵測之研究」，國立宜蘭大學土木工程學系研究所碩士論文：宜蘭。黃智遠，2008，「整合形狀及光譜資訊於房屋模型之變遷偵測」，國立中央大學土木工程學系碩士論文：桃園。鄒芳諭，2010，「以非量測性相機進行近景攝影測量探討」，國立交通大學土木工程學系碩士論文：新竹。楊旻憲，2009，「應用光達點雲資料與多譜影像處理技術萃取樹冠特徵資訊」，國立嘉義大學農學院森林暨自然資源研究所碩士論文：嘉義。鄭邦寧，2007，「使用空載光達點雲求定數值地表高程模型之小波法」，國立成功大學測量及空間資訊學系碩士論文：臺南。潘國樑，2009，『遙測學大綱：遙測概念、原理與影像判釋技術』第二版，臺北：科技圖書股份有限公司。蔡建成，2009，「以空載光達同步航空攝影製作高程模型品值探討」，國立交通大學土木工程學系碩士論文：新竹。賴君怡，2008，「整合空載光達資料與多光譜影像建立樹木模型—以成大校園為例」，國立成功大學測量及空間資訊學系碩士論文：臺南。蕭淳伊，2008，「應用空載光達資料與遙測影像推估樹林分布及體積」，國立成功大學測量及空間資訊學系碩士論文：臺南。謝幸宜，2011，「以自率光束法提升四旋翼UAV影像之定位精度」，國立政治大學地政學系碩士論文：臺北。二、外文參考文獻Baltsavias, E. P., 1999, “A comparison between photogrammetry and laser scanning”, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 83-94.Charaniya, A. P., R. Manduchi, and S. K. Lodha, 2004, ”Supervised Parametric Classification of Aerial LiDAR Data”, paper presented at the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Washington, DC, US, June 27-July 2.Detchev, I., A. Habib, and J. Y. Rau, 2011, “Image Matching for 3D Photogrammetric Reconstruction”, paper presented at the 32nd, Asian Conference on Remote Sensing, Taipei, Taiwan, October 3-7.Fritsch, D., A. M. Khosravani, A. Cefalu, and K. Wenzel, 2011, “Multi-Sensors and Multiray Reconstruction for Digital Preservation”, paper presented at the 53rd, Photogrammetric Week, Stuttgart, Germany, September 5-9.Henricsson, O., F. Bignone, W. Willuhn, F. Ade, O. Kübler, E. Baltsavias, S. Mason, and A. Grün, 1996, ‚“Project AMOBE: Strategies, Current Status and Future Work”, International Archives of Photogrammetry and Remote Sensing, paper presented at the 18th ISPRS Congress, Vienna, Austria, July 9-19. Haala, N., and C. Brenner, 1999, “Extraction of buildings and trees in urban environments”, ISPRS Journal of Photogrammetry & Remote Sensing, 54: 130-137.Hoegner, L., and U. Stilla, 2008, “Case Study of the 5-point Algorithm for Texturing Existing Building Models from Infrared Image Sequences”, International Archives of Photogrammetry, Remote Sensing and Spatial Geoinformation Sciences, 37(B3B): 479-484.Hoegner, L., and U. Stilla, 2009, “Thermal leakage detection on building facades using infrared textures generated by mobile mapping”, paper presented at Joint Urban Remote Sensing Event 2009, Shanghai, China, May 20-22.Jensen, J. 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TOPCON Positioning Systems Inc.Vosselman, G., and H. G. Maas, 2010, Airborne and Terrestrial Laser Scanning, Scotland, UK: Whittles Publishing.Wolf, P. R., and B. A. Dewitt, 2004, Elements of Photogrammetry With Applications in GIS, 3rd Edition, US: McGraw-Hill.三、網頁參考文獻iWitness (2009). Frequently Asked Questions. Retrieved May 24, 2012 from iWitness on the World Wide Web: http://www.iwitnessphoto.com/iwitness/faqs.htmlPhotometrix (2010, August). Users Manual for iWitness and iWitnessPRO. Retrieved January 9, 2012 from World Wide Web: http://www.photometrix.com.au zh_TW
