以往，航測影像發展主要為製圖目的而設計，一般較少應用於地物覆蓋之分類應用，近年來，航空數位相機之問世，其具紅外波段且高達10cm之的超高空間解析度，使得利用航測影像於都市區之特徵提取與分類成為可能。然而，因都市區地物特徵非常複雜，多光譜影像應用在都市區的地物分類能力上仍然有不足之處，其所面臨的困難包括樹木與草地之間的鑑別、或因建物屋頂成份的多樣性與陰影效果所造成的建物錯誤分類、或因道路上有汽車所造成的道路錯誤分類。近年來，空載光達(LiDAR)資料已經與其他遙測資料整合，以獲得更佳的分類結果，由LiDAR觀測資料處理產生之數值地表高度模型(DSMs)扣除數值地形高度模型(DEMs)所得之標準化數值地表高度模型(nDSMs)資料，成為重要的關鍵要素，本研究提出一套自適應性原始資料與趨勢面基礎的LiDAR資料地物濾除演算法以產生DEMs，作為產生nDSMs之基礎，由實驗結果證明，本演算法不但可以成功地將都市區、森林區與混合區地物予以濾除，且由所產生的DEMs相對精度或絕對精度測試結果證明，其精度均在LiDAR原始測量資料的精度範圍內。在使用航測影像應用於都市區分類方面，本研究先使用最大似然分類法(MLC) 進行實驗，以空載光達資料與航測影像找出最適合於都市區分類的特徵向量，與單純使用全彩RGB影像與多光譜影像的分類結果相比，額外加入光達高度資訊可分別改善分類整體精度達28與18%，證明高度資訊成為除了紅外影像之外，另一項提高都市區分類正確性之關鍵因素。為進一步改善分類效能，本研究提出以知識庫為基礎的分類系統(KBCS)，包括三層式高度規則式分類方法、”柏油路-植被-非植被 (A-V-N)” 分類模型與知識庫基礎的分類結果校正(KBC)。KBCS與MLC以及物件基礎分類(OBC)相比較，在統計精度上顯著增加影像的整體精度精度約12與7%，在視覺感知上明顯改善MLC分類影像上難以避免的雜訊與斑點，與OBC相比，分類結果也增加了視覺上的細節層次。

Multi-spectral Satellite imagery, among remotely sensed data from airborne and spaceborne platforms, contained the NIR band information is the major source for the land- cover classification. The main purpose of aerial imagery is for thematic land-use/land-cover mapping which is rarely used for land cover classification. Recently, the newly developed digital aerial cameras containing NIR band with up to 10cm ultra high resolution makes the land-cover classification using aerial imagery possible. However, because the urban ground objects are so complex, multi-spectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, aerial LiDAR (ULiUght UDUetection UAUnd URUanging) data have been integrated with remotely sensed data to obtain better classification results. The LiDAR-derived normalized digital surface models (nDSMs) calculated by subtracting digital elevation models (DEMs) from digital surface models (DSMs) becomes an important factor for urban classification. This study proposed an adaptive raw-data-based, surface-based LiDAR data-filtering algorithm to generate DEMs as the foundation of generating the nDSMs. According to the experiment results, the proposed adaptive LiDAR data-filtering algorithm not only successfully filters out ground objects in urban, forest, and mixed land cover areas but also derives DEMs within the LiDAR data measuring accuracy based on the absolute and relative accuracy evaluation experiments results. For the aerial imagery urban classification, this study first conducted maximum likelihood classification (MLC) experiments to identify features suitable for urban classification using LiDAR data and aerial imagery. The addition of LiDAR height data improved the overall accuracy by up to 28 and 18%, respectively, compared to cases with only red–green–blue (RGB) and multi-spectral imagery. It concludes that the urban classification is highly dependent on LiDAR height rather than on NIR imagery. To further improve classification, this study proposes a knowledge-based classification system (KBCS) that includes a three-level height, “asphalt road, vegetation, and non-vegetation” (A–V–N) classification model, rule-based scheme and knowledge-based correction (KBC). The proposed KBCS improved overall accuracy by 12 and 7% compared to maximum likelihood and object-based classification, respectively. The classification results have superior visual interpretability compared to the MLC classified image. Moreover, the visual details in the KBCS are superior to those of the OBC without involving a selection procedure for optimal segmentation parameters.

論文提要 i
Abstract vi
中文摘要 viii
誌謝 ix
Table of Contents xi
List of Tables xiv
List of Figures xv
List of Acronyms xvii
Chapter 1 Introduction 1
1-1 Motivation 1
1-2 Research Objectives 3
1-3 Research Questions 4
1-4 Research Methodology 5
1-5 Structure of the Thesis 8
Chapter 2 Literature Review 10
2-1 Urban Feature Extraction 10
2-1-1 LiDAR Data Urban Feature Extraction 11
2-1-2 LiDAR Data Building Extraction 12
2-1-3 LiDAR Data Road Extraction 13
2-1-4 Vegetation Feature Extraction 15
2-1-5 LiDAR-Based Vegetation Feature Extraction 17
2-2 High Resolution Image Classification 19
2-2-1 Pixel-Based Classification 19
2-2-2 Object-Based Classification 20
2-3 Knowledge-Based Classification 20
2-3-1 Early Development of Knowledge-Based Approaches 22
2-3-2 Recent Development of Knowledge-Based Approaches 23
2-4 LiDAR Data Filtering 25
2-4-1 Digital Elevation Models 25
2-4-2 LiDAR Data Filtering Development 26
2-4-3 LiDAR Data Filtering Difficulties 27
2-4-4 LiDAR Data Filtering Approaches 27
2-5 Summary 30
Chapter 3 Knowledge Base Construction 31
3-1 Prior Knowledge Rules 34
3-1-1 Three-Level Height Rules (rules P1–P3) 34
3-1-2 Area Analysis Rule (Rule P4) 35
3-2 Road Discriminative Model (RDM) 36
3-2-1 LiDAR Intensity Road Rule (Rule R1) 36
3-2-2 Smoothness Road Rule (Rule R2) 38
3-2-3 Penetrability Road Rule (Rule R3) 39
3-2-4 Road Image Holes Filling Procedure 40
3-2-5 Narrow Lanes Correction 44
3-3 Integrated Vegetation Discriminative Model (IVDM) 48
3-3-1 NDVI Vegetation Rules (Rules V1 and V2) 48
3-3-2 LiDAR–TVI Vegetation Rule (Rules V3 and V4) 51
3-3-3 ND Tree Rule (Rule V5) 54
3-3-4 AIS Tree Rule (Rule V6) 56
3-3-5 Global Thresholding 60
3-4 Building Correction Rules (Rule B1 and Rule B2) 60
Chapter 4 Three-Level Rule-Based Classification 63
4-1 Three-Level Selection and Definition 63
4-2 A–V–N Classification Principle 66
4-3 Knowledge-Based Correction (KBC) 67
4-3-1 Residual Reposition 67
4-3-2 Temporal Adjustment 71
4-3-3 Ground-Feature Generalization 73
4-3-4 Shape index Analysis 75
Chapter 5 Implementations for the Proposed KBCS 76
5-1 Matlab Implementation for the proposed KBCS 78
5-1-1 Low-Height Level Rule-Based Classification 79
5-1-2 Mid-Height Level Rule-Based Classification 84
5-1-3 High-Height Level Rule-Based Classification 88
5-1-4 Knowledge-Based Correction Module 91
5-2 EC-ERDAS Implementation for the proposed KBCS 92
5-3 eCognition Implementation for the proposed KBCS 97
Chapter 6 Aerial LiDAR Data Processing 101
6-1 LiDAR data filtering and terrain recovery 101
6-1-1 Indexing Raw LiDAR Data 101
6-1-2 Patchwise Second-order Polynomial Surface Approximation 104
6-1-3 LiDAR Data Filtering and Terrain Recovery 106
6-1-4 Implementation 109
6-2 Digital Elevation Models Accuracy Evaluation Experiments 114
6-2-1 LiDAR Data-Filtering Performance Test Experiment 114
6-2-2 Filtering Parameters Test Experiment 118
6-2-3 Evaluation of DEM Accuracy 120
6-3 Normalized Digital Surface Models Generation 121
6-4 Discussions 129
6-5 Summary 130
Chapter 7 Experiments and Analysis 132
7-1 Study Areas and Data Sets 132
7-2 Image Source Selection Experiment 141
7-2-1 MLC Classification 141
7-2-2 MLC Experiment Cases 143
7-2-3 Experiment Results and Discussions 144
7-3 KBCS Performance Evaluation Experiments 175
7-3-1 The OBC Classification 175
7-3-2 The KBCS Classification 176
7-3-3 The OBC and KBCS Experiments 177
7-3-4 Experiment Results and Discussions 178
Chapter 8 Conclusions and Recommendations 182
8-1 Conclusions 182
8-2 Contributions 183
8-3 Recommendations for future works 187
References 189
Appendix A Program List 197
Appendix B Accuracy Assessment Results 201

Alharthy, A. and J. Bethel, 2002. Heuristic Filtering and 3D Feature Extraction from LIDAR Data. In ISPRS Commission III, Symposium 2002 September 9-13, 2002, Graz, Austria, Pages A-29 ff (6 pages).
Arefi H., M. Hahn, and J. Lindenberger, 2003. Lidar data classification with remote Sensing tools. Proceedings of the ISPRS Commission IV Joint Workshop Challenges in Geospatial Analysis, Integration and Visualization II, September 8-9, 2003, Stuttgart, Germany, pp. 131-136.
Axelsson, P., 1999. Processing of laser scanner data – algorithms and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3): 138-147.
Baatz, M., U. Benz, S. Dehghani, and M. Heynen, 2004. eCognition User Guide 4, Definiens Imagine GmbH, Munchen, Germany.
Baltsavias, P. E. and A. Gruen, 2003. A comparison of aerial photos, lidar and IKONOS for monitoring cities. Remotely Sensed Cities (Mesev, V., editor). Taylor & Francis, New York, pp. 47-84.
Blaschke, T., M. Conradi and S. Lang, 2001. Multi-scale image analysis for ecological monitoring of heterogeneous, small structured landscapes, Proceedings of SPIE, Toulouse, pp. 35-44.
Brunn, A. and U. Weidner, 1997. Extracting Buildings from Digital Surface Models. International Archives of Photogrammetry and Remote Sensing. 32 (3-4W2), pp. 27-34
Carlotto, M. J., V. T. Tom, P. W. Baim and R. A. Upton, 1984. Knowledge-Based Multispectral Image Classification. SPIE Vol. 504. Application of Digital Image Processing VII, pp. 45-53
Carleer, P. A., O. Debeir, and E. Wolff, 2005. Assessment of Very High Spatial Resolution Satellite Image Segmentations. Photogrammetric Engineering & Remote Sensing, 71(11): 1285–1294.
Charaniya, A., R. Manduchi, and S. Lodha, 2004. Supervised Paramteric Classification of Aerial LIDAR Data. Conference on Computer Vision and Pattern Recognition Workshop Vol. 3, Washington DC, June 2004.
Civco, D.L., 1989. Knowledge-based land use and land cover mapping. Proc. of the 1989 Annual Meeting of the American Society for Photogrammetry and Remote Sensing, Baltimore, MD. pp. 276-291.
Clode, S., P. J. Kootsookos and F. Rottensteiner, 2004. The Automatic Extraction of Roads from LIDAR data. In: ISPRS 2004, Istanbul, Turkey.
Cohen, Y. and M. Shoshany, 2002. A national knowledge-based crop recognition in Mediterranean environment. International Journal of Applied Earth Observation and Geoinformation, 4(1): 75–87.
Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46.
Congalton, R.G. and R. A. Mead, 1983. A Quantitative Method to Test for Consistency and Correctness in Photointerpretation. Photogrammetric Engineering & Remote Sensing, 49(1): 69-74.
Deering, D. W., J. W. Rouse, R. H. Haas, and J. A. Schell, 1975. Measuring forage production of grazing units from Landsat MSS data, Proc., 10th International Symposium on Remote Sensing of Environment ERIM 2, pp. 1169-1178.
Estes, E. J., C. Sailer, and L. R. Tinney. 1986. Applications of Artificial Intelligence Techniques to Remote Sensing. The Professional Geographer, 38(2): 133-141.
Elaksher, A. F. and J. S. Bethel, 2002. Reconstructing 3D buildings from LiDAR data. In ISPRS Commission III Symposium 2002 September 9-13, 2002, Graz, Austria, pp. A 102-A 107.
Elberink, S.O., and H.G. Maas, 2000. The use of anisotropic height texture measurements for the segmentation of airborne laser scanner data, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B3, Amsterdam, 2000, pp. 678-684.
Filin, S., 2002. Surface clustering from airborne laser scanning data. In ISPRS Commission III Symposium 2002 September 9-13, 2002, Graz, Austria, pp. A-119.
Fitzpatrick-Lins, K., 1981. Comparison of Sampling Procedures and Data Analysis for a Land-Use and Land-Cover Map. Photogrammetric Engineering and Remote Sensing, 47(3): 343-351.
Gonzalez, R. C. and R. E. Woods, 2002. Digital Image Processing, Second Edition. Prentice-Hall, New Jersey. pp. 609
Giarratano, J. and G. Riley, 1989. Expert systems : principles and programming, PWS-KENT Publishing Company, Boston, 632 p.
Guindon, B., Y. Zhang, and C. Dillabaugh, 2004. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sensing of Environment , 92(6): 218–232
Jensen, J. R., 2005, Remote Sensing of Environment: An Earth Resource Perspective, Prentice-Hall, Inc., 2nd Edition
Haala, N. and C. Brenner, 1999. Extraction of buildings and trees in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3): 130-137.
Hill, A. R., G. M. Smith, R. M. Fuller, and N. Veitch, 2002. Landscape modelling using integrated airborne multispectral and laser scanning data. International Journal of Remote Sensing, 23(11): 2327–2334.
Hinz, S., and A. Baumgartner, 2000. Road Extraction in Urban Areas supported by Context Objects. International Archives of Photogrammetry and Remote Sensing, Vol. 33, part B3.
Hodgson, E. M., J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, 2003. Synergistic Use of lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness. Photogrammetric Engineering & Remote Sensing, 69(9): 973–980.
Hopgood, A. A., 1993. Knowledge-Based Systems for Engineers and Scientists. CRC Press, Inc. Florida, U.S.A.
Houldcroft, C. J., C. L. Campbell, I. J. Davenport, R. J. Gurney, and N. Holden, 2005. Measurement of canopy geometry characteristics using LiDAR laser altimetry: a feasibility study, Geoscience and Remote Sensing, IEEE Transactions on, vol. 43, pp. 2270-2282.
Hu, Y., and C.V. Tao, 2003. Automatic extraction of digital terrain models and road networks using multiple returns lidar data, ASPRS Annual Conference, 3-9 May, Anchorage, AK, 12 p.
Hu, Y. and V. Tao, 2005. Hierarchical Recovery of Digital Terrain Models from Single and Multiple Return Lidar Data. Photogrammetric Engineering & Remote Sensing, 71(4): 425-434.
Huang, M.J., S. W. Shyue, L. W. Lee, 2006. Update Urban GIS Data by Feature Extraction From lidar Data and Aerial Digital Color Imagery. The Sixth International Conference on ASIA GIS (ASIA GIS 2006), Malaysia, March 2006.
Huang, M.J., S. W. Shyue, L. W. Lee, and C. C. Kao, 2007. A Knowledge-Based Approach to Urban-feature Classification Using Aerial Imagery with LiDAR Data. Photogrammetric Engineering and Remote Sensing, (In Press).
Hung, M.-C. and M. K. Ridd. 2002, A sub-pixel classifier for urban land cover mapping based on a maximum likelihood approach and expert system rules. Photogrammetric Engineering and Remote Sensing, 68(11): 1173-1180.
Hyyppa, J., O. Kelle, M. Lehikoinen, and M. Inkinen,2001. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners, Geoscience and Remote Sensing, IEEE Transactions on, vol. 39, pp. 969-975.
Irons, J.R., B.L. Markham, R.F. Nelson, D.L. Toll, D.L. Williams, R.S. Latty, and M.L. Stauffer, 1985. The effects of spatial resolution on the classification of Thematic Mapper data, International Journal of Remote Sensing, 6(8):1385–1403.
Kilian, J., N. Haala, and M. Englich, 1996. Capture and evaluation of airborne laser scanner data, International Archives of Photogrammetry and Remote Sensing, 31, B3.
Knabenschuh, M., and B. Petzold, 1999. Data Post-processing of Laser Scan Data for Countrywide DEM Production, Proceedings of the Photogrammetry Week, D. Fritsch and R. Spiller, Editors, pp. 233–240.
Kraus, K., and N. Pfeifer, 1998. Determination of terrain models in wooded areas with airborne laser scanner data, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 53, pp.193–203.
Laszlo, J. M., 1996. Computational Geometry and Computer Graphics in C++, Prentice-Hall, New Jersey.
Lee, H., and N.H. Younan, 2003. DEM extraction of LiDAR returns via adaptive processing, IEEE Transactions on Geoscience and Remote Sensing, 41(9): 2063–2069.
Leica Geosystems, 2006. Introduction to EARDAS IMAGINE Expert Classifier.
Li, J., 2006. Robust Rule-Based Prediction. IEEE Transactions on Knowledge and Data Engineering, 18(8): 1043-1054.
Liu, X. H., A.K. Skidmore, and H. V. Oosten, 2002. Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry & Remote Sensing, 56(4): 257– 268.
Lohmann, P., A. Kock, and M. Schaeffer, 2000. Approaches to the filtering of laser scanner data, International Archives of Photogrammetry and Remote Sensing, 33, Amsterdam.
Ma, R., 2005. DEM Generation and Building Detection from lidar Data. Photogrammetric Engineering and Remote Sensing, 71(7): 847-854.
Mass, H. G., 1999. Fast determination of parametric house models from dense airborne laser scanner data. International Workshop on Mobile Mapping Technology, Bangkok, Thailand, April 21-23, 1999, IAPRS Vol. 32, Part 2W1
Moritz, H., 1972. Advanced Least-Squares Methods, Reports of the Department of Geodetic Science, The Ohio State University, No. 175.
Nagao, M. and T. Matsuyama, 1980. A Structural Analysis of Complex Aerial of Photographs, Plenum Press, New York.
Optech, 2004. ALTM 30/70/100 User Manual, Toronto, Canada.
Ostu, N., 1979. A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Systems, Man, and Cyernetices. Vol. SMC-9, no. 1, pp. 62-66.
Petzold, B., P. Reiss, and W. Stoessel, 1999. Laser Scanning—Surveying and Mapping Agencies are Using a New Technique for the Derivation of Digital Terrain Models, ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3): 95-104.
Price, K., 1999. Road grid extraction and verification. International Archives of Photogrammetry and Remote Sensing, Vol. 32, part 3-2W5, pp. 101-106.
Rego, F. and B. Koch, 2003. Automatic classification of land cover with high resolution data of the Rio de Janeiro city Brazil comparison between pixel and object classification. Proceedings of The International archives of the photogrammetry, remote sensing and spatial information sciences, Carstens, J. ed., Regensburg, Germany, 27-29 June.
Repaka, R. S., D. Truax, E. Kolstad, and C. Hara, 2004. Comparing Spectral and Object Based Approaches for Classification and Transportation Feature Extraction from High Resolution Multispectral Imagery. ASPRS Annual Conference Proceedings 23-28 May 2004, Denver, Colorado, pp. 204-215.
Richards, J. A. and X. Jia, 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, 363p.
Ridd, M. K., 1995. Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities, International Journal of Remote Sensing, 16(12): 2165-2185.
Roggero, M., 2001. Airborne Laser Scanning: Clustering in Raw Data, International Archives of Photogrammetry and Remote Sensing, vol. XXXIV–3/W4 Annapolis, MD, pp.227-232.
Rouse, J. W. Jr., Haas, R., H., Deering, D. W., Schell, J. A., and J. C. Harlan, 1974. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect)of Natural Vegetation. NASA/GSFC Type III Final Report, Greenbelt, MD., 371.
Rottensteiner, F., J. Trinder, S. Clode, and K. Kubik, 2005. Using the Dempster-Shafer method for the fusion of lidar data and multispectral images for building detection, Information Fusion, 6(4): 282-300
Samadzadegan, F., 2004. Object Extraction And Recognition From Lidar Data Based On Fuzzy Reasoning And Information Fusion Techniques. IAPRS, 12-23 July, Istanbul, Turkey Vol. XXXV, part B7
Schickler, W., and A. Thorpe, 2001. Surface estimation based on LiDAR, Proceedings of the ASPRS Annual Conference, 23–27, April, St. Louis, Missouri, 2001
Shan, J. and A. Sampath, 2005. Urban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance, Photogrammetric Engineering & Remote Sensing, 71(2): 217–226.
Shepard, D., 1968. A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 ACM National Conference, pp. 517–524.
Sithole, G. and G. Vosselman, 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 59, pp. 85-101, 2004.
Söderman, U., S. Ahlberg, Å. Persson, and M. Elmqvist, 2004. Towards rapid 3D modelling of urban areas, The Second Swedish-American Workshop on Modeling and Simulation, February 2-3, 2004.
Song, J.H., S.H. Han, K. Yu, and Y. Kim, 2002. Assessing the possibility of land-cover classification using LiDAR intensity data, IAPRS, 9-13 September, Graz, vol. 34, 4 p.
Stefanov, W. L., M. S. Ramsey, and P. R. Christensen, 2001. Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment, 77(2): 173–185
Summy, K. R., C. R. Little, R.A. Mazariegos, J. H. Everitt, M. R. Davis, J. V. French, and A.W. Scott, 2003. Detecting Stress in Glasshouse Plants Using Color Infrared Imagery: A Potential New Application for Remote Sensing, Subtropical Plant Science, Vol. 55, pp. 51-58
Sun, W., V. Heidt, P. Gong, and G. Xu, 2003. Information Fusion for Rural Land-Use Classification with High-Resolution Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(4): 883-890.
Swain, P.H. and S. M. Davis (eds.), 1978. Remote sensing : The quantitative approach. N. Y., McGraw-Hill.
Syed, S., P. Dare and S. Jones, 2005. Automatic classification of land cover features with high resolution imagery and LIDAR data: an object-oriented approach. Proceedings of SSC2005 Spatial Intelligence, Innovation and Praxis: The national biennial Conference of the Spatial Sciences Institute, September, 2005.
Taubenböck, H., T. Esch, and A. Roth, 2006. An Urban Classification Approach Based on an Object–Oriented Analysis of High Resolution Satellite Imagery for a Spatial Structuring Within Urban Areas. 1st EARSeL Workshop of the SIG Urban Remote Sensing Humboldt-Universität zu Berlin, 2-3 March 2006.
Thomas, N., C. Hendrix, and R. G. Congalton, 2003. A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery. Photogrammetric Engineering & Remote Sensing, 69(9): 963–972.
Ton, J., J. Sticklen and A. K. Jain, 1991. Knowledge-Based Segmentation of Landsat Images. IEEE Trans Geosciences and Remote Sensing, 29(2): 222-232.
Vosselman, G., 2000. Slope Based Filtering of Laser Altimetry Data, International Archives of Photogrammetry and Remote Sensing, vol. XXXIII, Part B3, Amsterdam pp.935–942.
Vosselman, G. and H. Mass, 2001. Adjustment and Filtering of Raw Laser Altimetry Data, Proceedings OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Elevation Models, Stockholm.
Vosselman, G., and M. Gunst, 1997. Updating road maps by contextual reasoning. Proceedings 2nd Workshop on Automatic Extraction of Man-Made Objects from Aerial and Space Images, Ascona, May 5-9.
Vu, T. T., R.Yokoyama; Yamazaki, F., and M.Tokunaga, 2003. Wavelet-based system for classification of airborne laser scanner data, Geoscience and Remote Sensing Symposium. IGARSS '03. Proceedings. IEEE International, vol. 7 pp. 4404-406, 2003.
Wang, F. and R. Newkirk, 1988. A Knowledge-Based System for Highway Network Extraction. IEEE Transactions on Geoscience and Remote Sensing, 26(5): 525-531.
Wever, C. and J. Lindenberger, 1999. Experiences of 10 years laser scanning, Photogrammetry Week, pp. 125-132.
Wehr, A. and U. Lohr, 1999. Airborne Laser Scanning – An Introduction and Overview, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 54 pp.68-82.
Weidner, U., and W. Förstner, 1995. Towards automatic building extraction from high-resolution digital elevation models, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 50, pp.38–49.
Wentz, A. E., W. L. Stefanov, C. Gries, and D. Hope, 2006. Land use and land cover mapping from diverse data sources for an arid urban environment. Computers, Environment and Urban Systems, 30(1): 320–346.
Wharton, S. W., 1987. A Spectral Knowledge Based Approach for Urban Land Cover Discrimination. IEEE Trans Geosciences and Remote Sensing, Vol. 25. No. 3, pp. 272-282.
Wolf, R. P. and A. B. Dewitt, 2000. Elements of Photogrammetry with applications in GIS. Third ed., McGraw-Hill Book Co.
Zhang, K., S. Chen, D. Whitman, M. Shyu, J. Yan, C. Zhang, 2003. A progressive morphological filter for removing nonground measurements from airborne LiDAR data, IEEE Transactions on Geoscience and Remote Sensing, 41(4): 872- 882.
Zhang, K., J. Yan, and S.C. Chen, 2006. Automatic Construction of Building Foorprints From Airborne LIDAR Data. IEEE Transaction on Geoscience and remote sensing, 44(9): 2523-2533.