空載光達與航空攝影測量的整合應用愈來愈受到重視，其最重要的前置工作為統一光達點雲與空照影像的坐標系統，本文的主要目的即是以光達點雲的點特徵求解空照影像的外方位參數，如此光達點雲與空照影像便可建立在共同的坐標系統之中。從真實的空載光達點雲中，觀察到瀝青路面和交通標線之反射強度明顯不同，根據此一通性，本文設計一個擴充式最小二乘匹配法，以求定交通標線之線特徵參數，進一步求得直交的兩特徵線之交點，此法產生的交點群即可用作光達與影像整合之用。另外，由於本文使用的光達航帶寬度明顯小於空照影像航帶所涵蓋的地面寬度，在統一光達點雲與空照影像坐標基準之前，勢必要先連結多條光達航帶的點雲，本文利用共同掃瞄區裡的同名交點進行相鄰光達航帶點雲坐標基準偏差量之估計與改正。

光達航帶點雲坐標基準經改正後，同名交點之整體平面偏差量之均方根值從0.650m降至0.286m，符合光達點之平面先驗精度±0.25m。使用這些基準改正後的點位做為地控點(共30點)，施行空照影像光束法平差，整體空三精度達到±0.67個像元的次像元精度等級，驗證本文所提之方法應可行。

Recently, the issue of integrating airborne LiDAR and aerial photogrammetry has been in academic and industrial spotlight, because both technologies have their unique characteristics respectively and some advantages of airborne LiDAR can compensate for shortcomings of aerial photogrammetry, and vice versa. An absolute prerequisite for combining both data sets is to establish a common reference frame. The main objective of this study is to utilize the point features in LiDAR point cloud and aerial image to determine external orientation of aerial images defined in the LiDAR coordinate system. Hence, an algorithm based on Extended Least Squares Matching is designed to extract road marking lines from airborne LiDAR point cloud, according to the high contrast character of LiDAR reflectance between asphalt and road marking lines. After that, the intersection of two road marking lines can be solved as the point feature in LiDAR point cloud. Besides, the point features, as tie-point, also can be applied to adjust the systematic error between LiDAR strips before combining LiDAR point cloud and aerial images.

The Root Mean Square Error (RMSE) of the tie points between LiDAR strips is decreased from 0.650m to 0.286m after 3D conformal transformation. After that, the point features will be used as ground control points to determine the external orientation of aerial images by bundle block adjustment. In the study, there are 30 control points extracted from LiDAR point cloud and the standard deviation of unit weight in the bundle block adjustment reaches ±0.67 pixels.

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