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系統識別號 U0026-3007201211001100
論文名稱(中文) 張量分析應用於結合光達資料與地形圖重建建物模型的品質預估之研究
論文名稱(英文) Tensor Analysis for Quality Prediction of Building Model Reconstruction by Integration of LiDAR Data and Topographic Information
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 100
學期 2
出版年 101
研究生(中文) 林柏丞
研究生(英文) Bo-Cheng Lin
學號 p68991022
學位類別 博士
語文別 中文
論文頁數 149頁
口試委員 口試委員-史天元
口試委員-趙鍵哲
口試委員-曾義星
口試委員-林昭宏
指導教授-尤瑞哲
中文關鍵字 光達  地形圖  張量分析  資料融合 
英文關鍵字 LiDAR  topographic map  Tensor analysis  Data Fusion 
學科別分類
中文摘要 本研究應用張量分析於光達點雲的特徵萃取,並針對資料融合光達點雲與地形圖產製的建物模型進行品質預估。特徵萃取是從光達點雲中萃取屋頂面區塊與屋脊線段,屋頂面區塊的萃取是利用張量投票法(Tensor Voting Method, TVM)在光達點雲中建立張量場,每一個光達點可以經由特徵分析推論其隱含的幾何特徵資訊,並利用主特徵為種子點的區域成長法將具有平面特徵的點雲群聚在一起。屋脊線段則是利用已萃取的屋頂面區塊推論而得。此外,本文提出三個正規化的特徵強度指標以減少點雲數量對特徵辨識的影響。針對TVM萃取平面區塊的成果,除了第一類型與第二類型錯誤之外,還新增碎形錯誤與辨識能力兩項新指標來評估。

在資料融合光達點雲與地形圖產製建物模型的過程,本研究引入穩健權函式的最小二乘法來匹配光達資料的邊緣點與二維向量圖的輪廓線,使兩組資料轉換至相同的坐標系統。基於融合資料的不一致會造成建物模型被錯誤的重建,本研究提出利用光達邊緣點與地形圖輪廓線的殘差張量分析進行品質預估,目的是偵測在現有資料品質之下可能被重建錯誤的模型。然而,為了達到更細緻的建物模型,仍須手動或引入其他資訊的編修。實驗結果顯示,本研究所提的品質預估指標不僅提高自動化模型重建的可靠性,且減少人工檢核成果的時間。
英文摘要 This study first presents a novel method based on the tensor voting framework for extracting building features from airborne LiDAR data. For the extraction of roof patches, geometric features of LiDAR points are represented by a tensor field. A region-growing method with principal features is developed from the properties of eigenvalues and eigenvectors of the tensor field. The extraction of ridge lines is then inferred from the segmented roof patches. On top of that, three new indicators of the strength of features are proposed to reduce the effect of the number of points on feature identification, and a supervised method is developed to determine the threshold of planar feature strength for the region growing. Furthermore, Type I errors, Type II errors, fragmentation and discernment are used as quality indicators to represent the effectiveness of the proposed method.

Next, we present an algorithm to integrate the LiDAR data and topographic maps for 3D building model reconstruction and develop a quality prediction indicator by the residual tensor analysis. To reduce influence of errors while integrating, 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 automatic model reconstruction quality of the predictions. The results show that our method can achieve reliable results and save both time and expense on model reconstruction.
論文目次 中文摘要 I
ABSTRACT II
目錄 IV
表目錄 VII
圖目錄 VIII
第1章 緒論 1
1.1 研究背景與動機 1
1.2平面特徵萃取 3
1.3線特徵萃取 6
1.4單一資料來源重建模型 8
1.5資料融合 9
1.6資料套合 11
1.7建物模型的品質預估 13
1.8研究議題與方法 14
第2章 張量代數 16
2.1 向量空間 17
2.2 線性組合 17
2.3 維度與基底 18
2.4 愛因斯坦求和協定 18
2.5 內積與歐幾里德空間 19
2.6 正交基底 20
2.7 對偶空間 20
2.8 張量的定義 21
2.9 張量運算 23
2.10 張量性質 26
2.11 特徵值與特徵向量 29
2.12 隨機張量及其誤差傳播 30
2.13 張量與幾何特徵 33
第3章 應用張量投票法於平面萃取 35
3.1 張量投票 36
3.1.1 初始法向量 38
3.1.2 傳遞幾何資訊 41
3.2 特徵顯著性指標 42
3.3 資料結構 45
3.4 平面特徵 48
3.4.1 區域成長 49
3.4.2 平面擴展 54
3.5 線段特徵 56
3.5.1 屋脊線段 56
3.5.2 邊緣線段 58
3.6 與主軸分析法比較 59
3.6.1 主軸分析法 59
3.6.2 特徵顯著性指標 60
3.6.3 雜訊的影響 62
3.7 小結 65
第4章 資料套合與品質預估 67
4.1邊緣點萃取 68
4.2數學模型 69
4.3殘差張量 74
4.4建物重建 77
4.5殘差張量與重建模型 81
4.6系統概觀 84
第5章 實驗與分析 87
5.1平面特徵萃取 87
5.2向量與位置精度 95
5.3光達與地形圖的套合成果 98
5.4模型重建與殘差張量的結果分析 100
第6章 結論與建議 106
參考文獻 110
SUMMARY 119
自述 150
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