全波形光達記錄回波的連續波形，藉由波形分析得到更多的地表反射物理特性、地表細節及變化，提供較豐富及完整的地表資訊，有助於地形重建及地物判識。本研究分別使用對稱函數(高斯函數)與不對稱函數(韋伯函數)進行擬合波形，並進行原始資料與擬合成果兩者間的精度評估，分析不同擬合函數對於全波形光達訊號處理的適用性。研究中萃取的波形參數包含波寬、振幅、背向散射參數，光達幾何參數則包含高程、高程差、回波數、多重回波百分比，結合波形及幾何參數進行地物分類。本研究以光達特徵配合人工判識選取訓練區，並使用支持式向量機(Support Vector Machine, SVM)與隨機森林(Random Forest)兩種分類器進行地物分類，並就地物分類成果進行精度評估，藉此比較使用全波形光達及多重回波光達進行分類之精度。研究結果顯示，雖然使用韋伯函數之波形擬合殘差較小，但在波形峰值位置的萃取成果與高斯函數之差異有限，因此高斯函數為一個簡易有效之擬合函數。在地物分類方面，全波形光達所提供的背向散射參數為一顯著性高的特徵，另隨機森林分類法的成果相較於支持式向量機為佳。

Full-waveform (FWF) lidar receives one dimensional continuous signal. It offers useful information about the structure of the target. Therefore, the analysis of received signal of FWF lidar and obtaining the implicit information is helpful for land cover classification. In the processing of full waveform Lidar data, the waveform parameter extraction and analysis are the important steps. The major objective of this study is to analyze the received waveform and extract its parameters. We select Gaussian distribution as a symmetric function and Weibull distribution as an asymmetric function in waveform decomposition. Then, we calculate several accuracy assessment indicators between raw waveform data and fitting function for quality assessment. We use echo width, amplitude, backscatter cross-section coefficient, elevation, elevation difference, echo number, and echo ratio as waveform parameter of classification. After waveform parameter extraction, we employ Support Vector Machine (SVM) and Random Forests (RF) as classifier for land cover classification. This study employs echo width, amplitude, backscatter cross-section coefficients and other features for classification. Error matrix is used to compare the performance of the classifiers. The experimental results indicate that the accuracy of asymmetric function is slightly better than symmetric function. However, the extracted peak positions from the Gaussian and Weibull are very close. Moreover, Gaussian distribution is relatively simple and easy to implement in the waveform analysis. The result of land cover classification shows that waveform parameters are helpful for classification and Random Forests classifier is slightly better than SVM in our study cases.