小面立體視覺法 (Facets stereo vision，簡稱FAST Vision)是一種使用數位影像自動重建數值地表模型DSM和求定數位正射影像的數位影像匹配法。一般使用空載光達點雲製作DSM時，常須先透過人工藉由影像判釋地形特徵並從大量光達點雲篩選出地表點雲，且各掃描線間距較同一條掃描線內的點間距大，且無法詳實記錄地表特徵線，將造成較大的內插誤差。而FAST Vision將影像匹配、數值地表模型求定及正射影像計算三者同時完成，它在求逆過程中也會遇到所謂的劣態問題(Ill-posed Problems)，欲解決此一問題可引入空間中的約制條件進行正則化(regularization)處理。而本研究之目的即是要將光達點雲資料以特定模式加入小面立體視覺法，整合空載光達點雲資料和多張數位影像進行小面立體視覺計算，將光達點雲的高程資訊當作一種空間約制條件，探討其處理劣態問題、重建零階不連續物面及其提升計算速度的效果。研究成果顯示，有無引入光達模式的正射影像及輻射參數g0'、g1'成果無論在平坦計算區或是具有零階不連續面之計算區都有高吻合度。平坦區之DSM格點標準偏差之rms值引入光達模式後從0.29 m下降為0.07 m，而具有零階不連續面之計算區則從0.14 m下降為0.07 m。平坦區DSM成果與LiDAR DSM之差異不大，高程差值平均從0.014 m降為0.011 m、差值的rms值從0.036 m降為0.033 m，但有零階不連續區與LiDAR DSM之高程差值平均從0.579 m降為0.057 m，而差值的rms值也從0.635 m降為0.203 m。此外，在零階不連續區域計算時間可節省約40%，但在平坦區計算時間相差不到1秒。

Facets stereo vision (FAST Vision) is an image matching method, in which digital surface model (DSM) generation and ortho image computation are simultaneously done. DSM can also be generated by using airborne LiDAR points, from which LiDAR points on the interest surface must be first selected. It is often aided by manual image interpretation. Also, the interpolated DSM might have large interpolation errors due to the lack of break lines and larger interval between two neighboring laser scanning lines. This study will propose an approach for integration of airborne LiDAR points with image data for a more efficient FAST Vision computation. It will be examined whether the so-called ill-posed problem could be solved in a more robust manner by adding some proper geometrical constraints provided by the airborne LiDAR points into the regularization process.

Experimental results show that ortho images and linear transfer parameters in the computations with and without LiDAR points can be regarded as the same. After adding LiDAR points, the root mean square values of the standard deviations of heights on DSM grid points are decreased from 0.29m to 0.07m and from 0.14m to 0.07m in flat areas and in the area with break lines, respectively.

The height differences between DSMs determined by FAST Vision with LiDAR points and directly by interpolation using LiDAR points have significantly decreased after using LiDAR points. In areas with break lines, their averages and root mean square values are decreased from 0.579m to 0.057m and from 0.635m to 0.203m, respectively, for the DSMs determined by FAST Vision with and without LiDAR points. The computation time also reduces to 60% of the one for the FAST Vision computation without LiDAR points.

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