空載光達技術發展快速，已成為目前DEM的主要生產來源，由空載光達產製DEM必須經過非地面點濾除的處理，目前已有許多過濾方法被提出，所有演算法的設計主要是盡可能的保留地面點，不同方法或多或少都會有分類錯誤的現象。由於DEM應用的層面相當廣泛，如何評估已完成的DEM品質為一重要的課題，研究主要探討以空載光達資料產製DEM過程中對其品質的影響。
以空載光達產製DEM以往以過濾後的分類精度評斷成果或是點雲內插後採用抽樣點檢核其RMSE。研究中針對DEM產製過程的品質控管，使用ISPRS公開的光達資料進行產製DEM的品質評估，方法有三，一是分類精度，利用過濾後之誤差矩陣計算統計指標；二是高程比對，為點雲內插後相應網格高程值的差異，可計算RMSE等統計指標；三是針對非因地形起伏太大造成過濾錯誤產生的大誤差，利用地形梯度計算正規化的高程差異。結合上述方法綜合分析以空載光達產製DEM過程的品質評估。
本研究之實驗數據探討三個有關空載光達產製DEM品質評估之問題。首先探討點雲過濾成果評估方法之合適性，單純以分類精度統計值無法完全反映DEM之品質，應配合DEM差值之RMSE及直方圖來評估DEM品質。許多因素會交叉影響DEM精度。第二個問題是自動化過濾會因濾除概念對於地形適應性會有所不同，目前之自動化過濾方法產製DEM的品質難以達完善，應配合人工編修提昇DEM品質。最後探討自動化過濾後進行人工編修所能提升之精度，在所有測試區域中分類精度皆明顯提升，受限於編修時間及人員的經驗，依據產製DEM的品質要求，精度提升的程度及時間人力成本仍需生產者折衷取捨。

Airborne LiDAR has become the primary technology of DEM generation. In order to produce DEM from airborne LiDAR data, the major process is filtering non-ground points. Mane filtering methods have been developed. All filters are designed to keep as many correct ground points as possible. However, there is no perfect filter yet. Every filter more or less misclassifies some non ground points to ground point, or the other way round. Owing to the widespread application of DEM, how to assess the quality of a DEM product is an important topic. For this reason, the paper investigates the quality influences of process of DEM generation from airborne LiDAR data.
The quality of DEM generated from airborne LiDAR data is conventionally assessed with the classification accuracy of point cloud filtering, or checking the elevation differences of the sampling points. This paper focuses on the study of quality control of DEM generation process. Three methods applied to assess DEM quality were investigated using ISPRS LiDAR test datasets. The first method is the evaluation of filtering accuracy. The filtered results are evaluated using the error matrix and the corresponding statistics indices. The second method is elevation accuracy. The DEM difference between a generated DEM and the reference dataset is computed for the evaluation. The third method is to locate large errors using the normalized DEM differences in which DEM differences are normalized using terrain gradients. The combination of the three methods is suggested for the quality control of DEM generation from airborne LiDAR data.
The experiments conducted in this paper mainly investigate three issues about the quality control of DEM generation from airborne LiDAR data. The first issue is the suitable of quality assessment methods of DEM generation. The experimented results suggest that one can not solely rely on statistics indices of point cloud classification to assess DEM quality. The RMSE are histogram of DEM difference should be investigated as well. The second issue is that the performance of a point cloud filter may vary subject to the terrain types. After all, none of filter methods has been claimed to be perfect. Manual editing is, therefore, suggested to improve DEM quality. Finally, how manual editing may improve the quality of DEM generation is studied. In accordance with the demand of DEM quality, a producer need to take the time and labor consumption into account.

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