使用多張重疊影像進行逐像元之影像密匹配，獲得被攝影物體表面之大量高密度點雲，可更細緻地描述物表面之特徵，但由於其仍為離散點雲，故必須先找出具有意義之幾何特徵，包括物表面特徵點、線、面或體，才可提供後續應用所需的各種三維模型。另外，由密點雲萃取出幾何特徵亦可大幅降低資料量，故可降低電腦在後續的資料傳輸、儲存、處理、展示等應用之負擔。
本文提出長方體元演算法，使用30張空照影像經密匹配軟體SURE(SUrface REconstruction)產生密點雲，並分別以體元法及張量投票法萃取房屋表面特徵。長方體元法利用事先定義之長方體做為體元，對房屋表面密點雲進行最小二乘套合，可求得房屋模型參數和房屋表面的特徵點、線、面及體，並可應用於密點雲之偵錯、剔錯。張量投票法則利用張量分析獲得各點之特徵強度，並據此利用區域成長法進行點雲分群，最終獲得建物之幾何特徵。在實驗測試中，首先以模擬資料測試及分析點密度及點分布對於體元法參數及張量投票法求解品質之影響，再使用真實房屋表面的密點雲資料施行長方體元法平差計算，並以人工立體量測成果做為地面檢核資料，分析特徵萃取成果之品質。
張量投票法之模擬資料成果顯示，在建物12條邊線中，成功萃取出4條垂直地面邊線，建物平面則由許多零碎小區塊構成，另外，點密度過大易導致邊緣線萃取失敗。體元法之真實資料萃取成果顯示，在長方體形狀之房屋表面點雲中，其角點坐標與地面檢核資料的均方根差值RMSD為1.21~2.32 GSD之間，12條房屋表面邊線之萃取成果與地面檢核資料邊線之平均距離為1.03~5.05 GSD之間，距離的均方根值為0.05~1.11 GSD之間。而其點雲偵錯結果顯示，錯誤點約佔建物點雲總點數之2.11%，大部份分布於陰影區、鄰近長方體建物表面邊線區及細微構造處(如兩鄰近平行牆面間)。另外，以真實建物二為例，受到2條南北向航帶和2條東西向航帶共4條航帶11張空照影像密匹配之房屋得到的平均點密度(單位:pt./g.)分別為牆面0.32、0.36、0.95、1.47和屋頂面10.29及地面4.38。

In this thesis, Tensor Voting Method(TVM) and Voxel Method(VM) are used to extract the geometry features, such as points, lines, planes and volume, from the dense matching point clouds of buildings. Moreover, VM could be used to detect blunders in the dense point clouds. After the simulated data are used to analyze the factors that could affect on the result of these two methods, dense image matching point clouds of three buildings by using 30 aerial images are used to derive their geometry features. The extraction result of simulated data indicates that TVM is likely to fail to extract features possibly due to low signal-to-noise ratio of points on local planes of buildings. On the other hand, VM is tested to analyze how the density and distribution of dense points on building surface could affect feature extraction quality. The result of Voxel Method with the use of real data shows the RMSD of point coordinates determined is about 1.21~2.32 GSD by comparing with the corresponding ground check data. The edges determined by Voxel method and the check data have the distances of about 1.03~5.05 GSD and RMSD of about 0.05~1.11 GSD.

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