適應性點雲過濾演算法於空載光達資料產生數值高程模型之研究

An Adaptive Point Cloud Filtering Algorithm for DEM Generation from Airborne LIDAR Data

周富晨(Fu-Chen Chou) ； 曾義星(Yi-Hsing Tseng)

《航測及遙測學刊》 10卷1期 (2005/03) Pp. 71-86

https://doi.org/10.6574/JPRS.2005.10(1).6

摘要

目前空載光達最主要的應用為生產DEM，而空載光達所得之點雲資料包含了地表點及地物點，應用於生產DEM則須先將非地面點濾除。本研究以形態學理論為基礎，提出適應性點雲過濾演算法，目的在於過濾點雲中的非地面點，生產DEM。本演算法利用三維網格結構化點雲資料，計算地表大範圍之趨勢面及局部坡度，主要以適應性坡度的概念過濾點雲。經由實際資料測試，本演算法對多數地形狀況能有效濾除非地面點，獲得良好的過濾結果，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。

關鍵字

數值高程模型 ； 空載光達 ； 點雲 ； 過瀘演算法

並列摘要

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 feasibility of the proposed algorithm is tested by using some test data with different characteristics of topography. The algorithm is proved 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 improper filtering results.

並列關鍵字

DEM ； LIDAR ； Point Cloud ； Filtering