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分類器進行監督式分類。本研究使用了支持向量機(SVM)與單純貝氏分類器(NBC)兩種分類器，實驗方法亦分為三種，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， 最後比較其不同組合之分類成果。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) and Naive Bayes Classifier (NBC) 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.

內政部，2011。發展先進空載光達科技與應用工作案工作總報告書，共135頁，財團法人成大研究發展基金會執行。
內政部營建署，2003。市區道路人行道設計手冊。
王儷蓉，1998。高光譜資料之差異與類別分離度分析，國立成功大學測量工程學系博士論文。
包順安，1999。高光譜影像平滑與特徵萃取對分類精度之影響，國立成功大學測量工程研究所碩士論文。
林郁珊，2012。應用空載全波形光達資料於波形分析與地物分類，國立交通大學土木工程研究所碩士論文。
洪凱政，2009，應用多光譜影像多種特徵偵測崩塌地之研究，國立成功大學測量工程研究所碩士論文。
徐百輝，2003。小波轉換應用於高光譜影像光譜特徵萃取之研究，國立成功大學測量工程學系博士論文。
徐百輝、曾義星，2000。高光譜影像特徵萃取方法之探討，航測與遙測學刊，第五卷第二期，1-14頁。
高雄市政府環境保護局，2007。高雄市裸露地綠化暨露天燃燒管制計畫。
蘇竹君，2008，氣候變遷與土地利用改變對石門水庫集水區之流量與泥砂產量影響，碩士論文，國立中央大學土木工程學系
Alexander, C., Tansey, K., Kaduk, J., Holland, D., Tate, N.J., 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 65, pp. 423-432.
Bork, E.W., Su., J.G. 2007. Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: a meta analysis. Remote Sensing of Environment, 111 (1), pp. 11–24.
Bretar, F., Chauve,A., Bailly, J.-S., Mallet,C., Jacome, A.,2009. Terrain surfaces and 3D landcover classification from small footprint full-waveform lidar data: Application to badlands. Hydrology and Earth System Sciences,13,1531-1545.
Burges, C.J.C.,1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121–167.
Choi, E. and Lee, C（2003）, Feature extraction based on the Bhattacharyya distance. Pattern Recognition, 36,1703 – 1709.
Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37 (1), 35–46.
Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning (20), pp. 273-297.
Doneus, M., Briese,C., Fera,M. and Janner,M.,2008.Archaeological prospection of forested areas using full-waveform airborne laser scanning, Journal of Archaeological Science,Vol. 35, 882-893.
Friedman, N., Geiger, D., Goldszmidt, M.,1997. Bayesian Network Classifiers. Machine Learning, 29, 131–163.
Guo, L., Chehata, N., Mallet, C., Boukir, S., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011) ISPRS Journal of Photogrammetry and Remote Sensing, 66 (1), pp. 56-66.
Heinzel, J., Koch, B., 2011. Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13, pp. 152-160.
Hofton, M., Minster, J., Blair, J., 2000. Decomposition of laser altimeter waveforms. IEEE Transactions on Geoscience and Remote Sensing 38 (4), 1989–1996.
Hudak, A.T., Evans, J.S., Stuart, A.M., 2009. LiDAR utility for natural resource managers. Remote Sensing, 1 (2009), pp. 934–951
Jelalian, A.V.,1992. Laser Radar Systems. Artech House, Boston London.
Jutzi, B., Stilla, U., 2003. Laser pulse analysis for reconstruction and classification of urban objects. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34 (Part 3/W8), 151156.
Jutzi, B., Stilla, U., 2006. Range determination with waveform recording laser systems using a Wiener Filter, Remote Sensing and Spatial Information Sciences 61 (2), 95-107.
Kao, D.L., Kramer, M.G., Love, A.L., Dungan, J.L., Pang, A.T.,2005. Visualizing Distributions from Multi-Return Lidar Data to Understand Forest Structure. The Cartographic Journal Vol. 42 No. 1 pp. 35–47.
Kost, K., Loddenkemper, M., Petring, J., 1996. Airborne laserscanning, a new remote sensing method for mapping terrain. Third EARSeL Workshop on LiDAR remote sensing of land and sea, Tallinne, Estonia, pp. 89–96.
Landgrebe, D. and Biehl, L.,1997. An Introduction to Multispec.
Liang, X., Hyyppa,J., Matikainen, L., 2007. Deciduous-coniferous tree classification using difference between first and last pulse laser signatures. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, pp. 253-257.
Lin, Y.C., Mills, J., 2009. Integration of full-waveform information into the airborne laser scanning data filtering process. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (Part 3/W8), 224–229.
Lloyd, D., 1990.A phonological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sens., 11 (12), pp. 2269–2270.
Lohr, U., 1998. Laserscanning for DEM generation, GIS technologies and their environmental applications, Computational Mechanics Publications, Southampton, UK (1998), pp. 243–249
Mallet, C., Bretar, F., Roux, M., Soergel, U., Heipke, C., 2011. Relevance assessment of full-waveform lidar data for urban area classification. ISPRS Journal of Photogrammetry and Remote Sensing.
Matsatsinis, N.F., Samaras, A.P., 2000. Brand choice model selection based on consumers’ multicriteria preferences and experts’ knowledge. Computers & Operations Research(27), pp.689-707.
Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefsky, M.A., Cohen, W.B., Harmon, M.E., McKee, W.A., 1999. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sensing of Environment, Volume 67, issue 3, pp. 298–308.
Mongus, D., Žalik, B., 2012. Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 67, Pages 1–12.
Neuenschwander, A.L., Magruder, L.A., Tyler, M., 2009. Landcover classification of small-footprint, full-waveform lidar data. Journal of Applied Remote Sensing, Vol. 3, 033544.
Park, M.H., Stenstrom, M.K., 2006. Using satellite imagery for stormwater pollution management with Bayesian networks. WAT ER RESEARCH 40, pp. 3429-3438.
Persson, A., Soderman, U., Topel, J., Alhberg, S., 2005. Visualization and analysis of full-waveform airborne laser scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36 (Part3/W19), 103–108.
Ramesh, V. and Ramar, K.,2011. Classification of Agricultural Land Soils: A Data Mining Approach. Agricultural Journal 6, pp. 82-86.
Rottensteiner, F., 2003. Automatic generation of high-quality building models from LIDAR data. Computer Graphics and Applications, 23 (6), pp. 42–50
Song, J.H., Han, S.H., Yu, K., Kim, Y.I., 2002. Assessing the possibility of land-cover classification using LiDAR intensity data International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, 34 (Part 3B), pp. 259–262.
Soria, D., Garibaldi, J. M., Biganzoli, E., Ellis, I. O., 2008. A comparison of three different methods for classification of breast cancer data, Seventh International Conference on Machine Learning and Applications, IEEE Computer Society.
TerraSolid, 2011. TerraScan User’s Guide.
Theodoridis,S. and Koutroumbas, K.（2006）, Pattern Recognition（3rd ed.）,USA: Elsevier.
Wagner, W., A. Ullrich, V. Ducic, T. Melzer, ＆ N. Studnicka, 2006. Gaussian Decomposition and Calibration of a Novel Small-Footprint Full waveform Digitising Airborne Laser Scanner. ISPRS Journal of Photogrammetry and Remote Sensing, No. 2, pp. 100-112.
Wagner, W., Hollaus, M., Briese, C.,Ducic,V., 2008. 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. International Joutnal of Remote Sensing 29 (5), pp.1433-1452.
Wagner, W.,2010. Radiometric calibration of small-footprint full-aveform airborne laser scanner measurements: Basic physical concepts. ISPRS Journal of Photogrammetry and Remote Sensing 65, pp. 505-513.
Wang, C.K., 2012. Exploring weak and overlapped returns of a LiDAR waveform with a wavelet-based echo detector. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7.
Webb, G.I. and Pazzani, M.J., 1998. Adjusted Probability Naive Bayesian Induction . Advanced Topics in Artificial Intelligence Lecture Notes in Computer Science 1502, pp. 285-295