空載光達目前最主要的應用為DEM的生產，而空載光達所得之點雲資料為DSM，應用於生產DEM則須先將非地面點濾除。本研究的目的即為利用空載光達資料生產DEM，主要探討如何過濾點雲。
　　本研究提出「適應性點雲過濾演算法」，主要以坡度適應性的概念過濾點雲。本法以三維網格為基礎，建立點位的搜尋機制，並利用其篩選點雲，以擬合平面法(Fitting Plane)計算地表大範圍之趨勢面及局部坡度。過濾程序分兩階段進行：(1)粗略過濾及(2)細節過濾。粗略過濾利用地表大範圍之趨勢面，以門檻值的操作將遠離地表的點濾除，主要包括：大型地物以及樹木頂面的點。而細節過濾則由粗略過濾所保留的點，考量小範圍的點雲分佈，根據局部坡度進行過濾。經由上述兩階段的過濾程序，本法最後設計了迭代過濾，主要目的在於逐次修正趨勢面，避免趨勢面與地表過度的偏離。
　　本研究利用新竹地區空載光達資料作為實驗資料，選取不同類型的地形、地物探討本演算法的可行性。經由實際測試，本演算法在大多數情況下有效、可行，並能達到良好的過濾結果，唯在地形斷線處以及地形突然隆起或是下陷處，可能會將地面點過度濾除。而針對少部分過濾有誤的地方仍須以人工進行檢核編修，以確保DEM之成果品質。

　　DEM generation is the primary application of airborne Lidar. The point cloud provided by airborne Lidar not only represents the terrain surface, but also contains buildings, vegetation, or other ground objects. The major process of generating DEM from airborne Lidar is to filter out non-ground points from the point cloud data. The purpose of this study is to propose an adaptive filtering algorithm for DEM generation using airborne Lidar data.
　　The filtering algorithm is based on the principle of morphological filtering theory. To make the algorithm adaptive, a 3-D grid structure is used to organize point cloud data, so that the trend surface and local slope of the ground can be estimated. The algorithm is carried out in two steps: the rough filtering and the detailed filtering. The rough filtering is to remove the points far away from the trend surface by the pre-defined threshold. Then the remained points will be filtered again using estimated local slope in the detailed filtering. The two-step filtering can be performed iteratively, so that the trend surface is modified gradually in each step of iteration to obtain the optimal result.
　　The feasibility of the proposed algorithm is tested by using some test data with different characteristics of topography covering Hsinchu County. The algorithm is proud to be effective and practicable in most test cases, but in some cases some ground points in the rough terrain may be over-filtered. To assure the quality of DEM product, manual check and editing is still necessary against the improper filtering results.

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