隨著光達技術的發展，近年來商業型的空載光達系統已經能夠記錄雷射與地物交會之完整的反射強度變化，稱為全波形(full waveform)空載光達系統。相較於傳統光達僅能記錄少數的回波響應值(echoes)，全波形光達可完整地記錄雷射光行經物體間的反射強度(intensity)，且所記錄的波形除了可推算反射物距離外，亦隱含了反射物的物理性質，因此全波形光達具有更深遠的應用和潛力。地物表面的反射性質、幾何結構和粗糙度皆會影響雷射的反射波形，因此透過對全波形光達資料所記錄的波形進行分析，有助於解讀地物表面的型態，這些性質也提供了地物分類之依據。本研究針對從波形資料中偵測得之所有地物響應波形，分析各類地物響應波形特徵的特性，並交叉比對不同地物類別之波形特徵的可區分性，以利於選擇有效的分類特徵，並依據其分析成果，設計一套以波形為主的全波形光達資料分類方法與流程。 實驗資料包含三個廠牌之儀器(Leica、Riegl及Optech)，根據實驗區的主要地物類別，從正射影像挑選出欲分類目標類別，即植被、道路、裸露地、建物、草地農地等五類，並針對這些類別進行樣本選取與波形分類特徵分析。根據單響應及多響應的波形特徵分析結果，選擇適合的分類特徵，接著將選取的特徵輸入支持向量機(SVM)進行監督式分類。本研究之實驗方法分為三種，包含以響應為基礎、以波形為基礎與以波形為基礎並加入影像的分類法。實驗成果顯示相較於以響應為主的分類法，以波形為主的方法能提升約20%的分類精度，且加入影像後整體精度最高可達86%，對於地物的三維分類具有相當之潛力。

Thanks to the development of LiDAR technology, recording full waveform information of return laser signal has become available. Compared with the conventional LiDAR system, waveform LiDAR further encodes the intensity of return signal along the time domain, which enables the users to utilize the continuous return signal for the interpretation of ground objects. Potential of more applications than the use of traditional LiDAR can be expected with the use of full waveform LiDAR. A LiDAR waveform is a recorded energy of the backscattered laser pulse along the time domain. The shape of a waveform is formed according as the characteristics surface reflectance, geometric structure and roughness of the laser footprint. It would be possible to extract the information of surface characteristics from waveform data, and this information can be used for the classification of ground surface. This study focuses on the analysis of LiDAR full-waveform data. The effects of various ground objects and surfaces on the waveform data will be analyzed, and the reparability of waveform features among categories of ground objects will be identified. Based on this analysis, a classification approach is developed for LiDAR full-waveform data. The estimation of classification accuracy will be reported as well. The experiment data were collected with three airborne LiDAR systems of different brands, namely Leica、Riegl and Optech. The land cover objects of the experimental area are mainly categorized into road, canopy, grass & crop, bare ground and buildings. Waveform features were analyzed with respect to the single and multiple return laser paths samples, and waveform classification features were selected according to the analysis. Then, the supervised classification by using Support Vector Machine (SVM) was performed in three defined methods which include echo-based, waveform-based and waveform-based with images. The experiment results show that the overall accuracy of waveform-based method increases about 20% comparing to echo-based method and it can achieve 86% with the images. This study reveals the potential of 3D object classification using airborne LiDAR waveform data.