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研究生:林柔安
研究生(外文):Lin, Jou An
論文名稱:旋翼UAS影像密匹配建物點雲自動分群之研究
論文名稱(外文):Automatic clustering of building point clouds from dense matching VTOL UAS images
指導教授:邱式鴻邱式鴻引用關係
指導教授(外文):Chio, Shih Hong
學位類別:碩士
校院名稱:國立政治大學
系所名稱:地政研究所
學門:社會及行為科學學門
學類:公共行政學類
論文種類:學術論文
畢業學年度:103
語文別:中文
論文頁數:87
中文關鍵詞:影像密匹配無人機系統Mean shift分群法
外文關鍵詞:dense image matchingUnmanned Aerial System (UAS)mean shift clustering
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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.
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
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