測量車影像序列富含測量車行經路線帶狀的空間資訊，配合測量車之定位、定向系統，每張影像之內外方位可於獲取影像後，加上率定資料求得，因此可透過多影像匹配以前方交會計算應用者感興趣之地物的三維坐標，有效率地蒐集空間資訊。透過影像像點、投影中心及物點的共線關係，重疊影像間的共軛像點可透過共線關係獲取該點位之物空間坐標。利用影像匹配進行共軛點的獲取是比較經濟的做法，尤其測量車影像序列這樣龐大的資料量。但測量車影像存在尺度變化、視角不同及遮蔽問題可能影響點位的正確匹配，因此本研究將影像匹配從常見的像空間匹配轉換至物空間進行，配合假設面的建立改善尺度變化及視角不同產生的問題。
由於測量車影像序列於行進中拍攝，根據物體與相機的距離遠近，在影像中有前景及背景的分別，當前景地物遮住了背景地物，則產生遮蔽問題。本研究發展影像挑選機制，確保用以匹配的影像皆包含該目標點位，將遭遮蔽的影像過濾。另外，遭前景遮蔽的背景隨著拍攝位置的不同而變化，變化越劇烈的背景資訊，亦對點位的正確匹配帶來影響，因此本研究透過視窗分割，減輕背景對影像匹配的不良影響。
由實驗結果顯示，本研究提出的方法對街道上之特徵明顯的點位，幾乎可以獲得良好之匹配結果，其成功匹配率大約可達80%至90%，而所得之點位物空間坐標，與驗證場上控制點之地面測量坐標比較，所求坐標可達約10公分的精度，約50公分的準確度，足以應付一般使用者的需求及大比例尺之地理資訊系統的資料建置，若提升定位及定向系統的穩定性，將可使準確度更理想。

Image sequences acquired with a Mobile Mapping System (MMS) contain rich information along the trajectory. The coordinates of interested points can be determined by space forward intersection of the images, whose interior and exterior orientation are determined from the navigation and calibration data. Hence the acquisition of spatial data is much efficient. The basic idea to obtain the 3D coordinates of conjugate points from overlap images is to rebuild the geometric relation between images. Obtaining conjugate points of image sequences by image matching is much more efficient than manual measurement. However the factors of scale variations, different field of view, and occlusion may result in incorrect matching. In this research, image matching in object space with virtual surface is proposed to overcome the problems of matching in image space.
Due to the image sequences are acquired during the vehicle moving, the distance variations from objects to cameras cause differences between foreground and background in images. Occlusion problems also cause difficulties of image matching. The process to filter out occluded images is proposed, and the remaining useful images are kept for multiple image matching. In addition, multiple windows matching, in which the target point is located in different positions, is developed to ease the problems caused by complex backgrounds.
The test results of the experiments show the proposed method can deliver correct matching results good to about 80% to 90%. The determined coordinates are about 10 cm in precision, and about 50 cm in accuracy. The quality of point determination is good enough for general applications of GIS data acquisition. Accuracy can be improved, if the positioning and orientation system of the MMS can be improved.

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