Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification

International Journal of Remote Sensing

Volumn 36, Issue 6, 2015, Pages 1618-1644

  Man, Qixia ^a   Dong, Pinliang ^b   Guo, Huadong ^c  

^a EAST CHINA NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF NORTH TEXAS   (United States)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA FUSION; LAND USE; MAXIMUM LIKELIHOOD; OPTICAL RADAR; PIXELS; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLASSIFICATION RESULTS; DIGITAL SURFACE MODELS; LANDUSE CLASSIFICATIONS; LIDAR , HYPERSPECTRAL; LIGHT DETECTION AND RANGING; MAXIMUM LIKELIHOOD CLASSIFICATIONS; NORMALIZED DIFFERENCE VEGETATION INDEX; REMOTELY SENSED DATA;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; AIRBORNE SENSING; CLASSIFICATION; LAND USE; LIDAR; NDVI; PIXEL; SPECTRAL ANALYSIS; URBAN AREA;

EID: 84926163182     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1015657     Document Type: Article

References (87)

1. Aguirre-Gutiérrez, J., A. C. Seijmonsbergen, and J. F. Duivenvoorden. 2012. “Optimizing Land Cover Classification Accuracy for Change Detection, a Combined Pixel-Based and Object-Based Approach in a Mountainous Area in Mexico.” Applied Geography 34: 29–37. doi:10.1016/j.apgeog.2011.10.010.
2. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Washington, DC: US Government Printing Office.
3. Antonarakis, A. S., K. S. Richards, and J. Brasington. 2008. “Object-Based Land Cover Classification Using Airborne Lidar.” Remote Sensing of Environment 112: 2988–2998. doi:10.1016/j.rse.2008.02.004.
4. Awrangjeb, M., M. Ravanbakhsh, and C. S. Fraser. 2010. “Automatic Detection of Residential Buildings Using LIDAR Data and Multispectral Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 65: 457–467. doi:10.1016/j.isprsjprs.2010.06.001.
5. Ben-Dor, E., N. Levin, and H. Saaroni. 2001. “A Spectral Based Recognition of the Urban Environment Using the Visible and Near-Infrared Spectral Region (0.4-1.1 M). A Case Study over Tel-Aviv.” International Journal of Remote Sensing 22: 2193–2218. doi:10.1080/014311601300190677.
6. Bhaskaran, S., S. Paramananda, and M. Ramnarayan. 2010. “Per-Pixel and Object-Oriented Classification Methods for Mapping Urban Features Using IKONOS Satellite Data.” Applied Geography 30: 650–665. doi:10.1016/j.apgeog.2010.01.009.
7. Binal, C., and N. S. R. Krishnayya. 2009. “Classification of Tropical Trees Growing in a Sanctuary Using Hyperion (EO-1) and SAM Algorithm.” Current Science 96: 1601–1607.
8. Brennan, R., and T. L. Webster. 2006. “Object-Oriented Land Cover Classification of Lidar-Derived Surfaces.” Canadian Journal of Remote Sensing 32: 162–172. doi:10.5589/m06-015.
9. Buján, S., E. González-Ferreiro, F. Reyes-Bueno, L. Barreiro-Fernández, R. Crecente, and D. Miranda. 2012. “Land Use Classification from LiDAR Data and Ortho-Images in a Rural Area.” The Photogrammetric Record 27: 401–422. doi:10.1111/j.1477-9730.2012.00698.x.
10. Carrão, H., P. Gonçalves, and M. Caetano. 2008. “Contribution of Multispectral and Multitemporal Information from MODIS Images to Land Cover Classification.” Remote Sensing of Environment 112: 986–997. doi:10.1016/j.rse.2007.07.002.
11. Cavalli, R. M., L. Fusilli, S. Pascucci, S. Pignatti, and F. Santini. 2008. “Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy).” Sensors 8: 3299–3320. doi:10.3390/s8053299.
12. Charaniya, A. P., R. Manduchi, and S. K. Lodha. 2004. “Supervised Parametric Classification of Aerial Lidar Data.” IEEE Conference on Computer Vision and Pattern Recognition Workshop, CVPRW’04, Washington, DC, June27–July 2. doi:10.1109/cvpr.2004.446.
13. Chen, Q. 2007. “Airborne Lidar Data Processing and Information Extraction.” Photogrammetric Engineering and Remote Sensing 73: 109–112.
14. Chen, Y., W. Su, J. Li, and Z. Sun. 2009. “Hierarchical Object Oriented Classification Using Very High Resolution Imagery and Lidar Data over Urban Areas.” Advances in Space Research 43: 1101–1110. doi:10.1016/j.asr.2008.11.008.
15. Cho, M. A., A. Skidmore, F. Corsi, S. E. Van Wieren, and I. Sobhan. 2007. “Estimation of Green Grass/Herb Biomass from Airborne Hyperspectral Imagery Using Spectral Indices and Partial Least Squares Regression.” International Journal of Applied Earth Observation and Geoinformation 9: 414–424. doi:10.1016/j.jag.2007.02.001.
16. Clapham Jr., W. B. 2003. “Continuum-Based Classification of Remotely Sensed Imagery to Describe Urban Sprawl on a Watershed Scale.” Remote Sensing of Environment 86: 322–340. doi:10.1016/S0034-4257(03)00076-2.
17. Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37: 35–46. doi:10.1016/0034-4257(91)90048-B.
18. Cortes, C., and V. Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20: 273–297. doi:10.1007/BF00994018.
19. Dalponte, M., L. Bruzzone, and D. Gianelle. 2008. “Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas.” IEEE Transactions on Geoscience and Remote Sensing 46: 1416–1427. doi:10.1109/TGRS.2008.916480.
20. Debes, C., A. Merentitis, R. Heremans, J. Hahn, N. Frangiadakis, T. Van Kasteren, W. Liao, R. Bellens, A. Pizurica, S. Gautama, W. Philips, S. Prasad, Q. Du, and F. Pacifici. 2014. “Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7: 2405–2418. doi:10.1109/JSTARS.2014.2305441.
21. Erdody, T. L., and L. M. Moskal. 2010. “Fusion of Lidar and Imagery for Estimating Forest Canopy Fuels.” Remote Sensing of Environment 114: 725–737. doi:10.1016/j.rse.2009.11.002.
22. Fauvel, M., J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson. 2008. “Spectral and Spatial Classification of Hyperspectral Data Using Svms and Morphological Profiles.” IEEE Transactions on Geoscience and Remote Sensing 46: 3804–3814. doi:10.1109/TGRS.2008.922034.
23. Flood, M. 2001. “LIDAR Activities and Research Priorities in the Commercial Sector.” International Archives of Photogrammetry and Remote Sensing 34: 3–7.
24. Foody, G. M. 2002. “Status of Land Cover Classification Accuracy Assessment.” Remote Sensing of Environment 80: 185–201. doi:10.1016/S0034-4257(01)00295-4.
25. Franke, J., D. A. Roberts, K. Halligan, and G. Menz. 2009. “Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of Hyperspectral Imagery for Urban Environments.” Remote Sensing of Environment 113: 1712–1723. doi:10.1016/j.rse.2009.03.018.
26. Gallego, F. J., E. Carfagna, and B. Baruth. 2010. “Accuracy Objectivity and Efficiency of Remote Sensing for Agricultural Statistics.” Agricultural Survey Methods 193–211. doi:10.1002/9780470665480.ch12.
27. Gallego, F. J., N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, and O. Kussul. 2014. “Efficiency Assessment of Using Satellite Data for Crop Area Estimation in Ukraine.” International Journal of Applied Earth Observation and Geoinformation 29: 22–30. doi:10.1016/j.jag.2013.12.013.
28. Galvão, L. S., A. R. Formaggio, and D. A. Tisot. 2005. “Discrimination of Sugarcane Varieties in Southeastern Brazil with EO-1 Hyperion Data.” Remote Sensing of Environment 94: 523–534. doi:10.1016/j.rse.2004.11.012.
29. Gamba, P., and B. Houshmand. 2002. “Joint Analysis of SAR, LIDAR and Aerial Imagery for Simultaneous Extraction of Land Cover, DTM and 3D Shape of Buildings.” International Journal of Remote Sensing 23: 4439–4450. doi:10.1080/01431160110114952.
30. Geerling, G. W., M. Labrador-Garcia, J. Clevers, A. M. J. Ragas, and A. J. M. Smits. 2007. “Classification of Floodplain Vegetation by Data Fusion of Spectral (CASI) and Lidar Data.” International Journal of Remote Sensing 28: 4263–4284. doi:10.1080/01431160701241720.
31. Gerke, M., and J. Xiao. 2014. “Fusion of Airborne Laser Scanning Point Clouds and Images for Supervised and Unsupervised Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 78–92. doi:10.1016/j.isprsjprs.2013.10.011.
32. Giannini, M. B., P. Merola, and A. Allegrini. 2012. “Texture Analysis for Urban Areas Classification in High Resolution Satellite Imagery.” Applied Remote Sensing Journal 2: 2.
33. Guo, L., N. Chehata, C. Mallet, and S. Boukir. 2011. “Relevance of Airborne Lidar and Multispectral Image Data for Urban Scene Classification Using Random Forests.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 56–66. doi:10.1016/j.isprsjprs.2010.08.007.
34. Herold, M., and D. A. Roberts. 2005. “Spectral Characteristics of Asphalt Road Aging and Deterioration: Implications for Remote-Sensing Applications.” Applied Optics 44: 4327–4334. doi:10.1364/AO.44.004327.
35. Herold, M., D. A. Roberts, M. E. Gardner, and P. E. Dennison. 2004. “Spectrometry for Urban Area Remote Sensing-Development and Analysis of a Spectral Library from 350 to 2400 Nm.” Remote Sensing of Environment 91: 304–319. doi:10.1016/j.rse.2004.02.013.
36. Hill, R. A., and A. G. Thomson. 2005. “Mapping Woodland Species Composition and Structure Using Airborne Spectral and Lidar Data.” International Journal of Remote Sensing 26: 3763–3779. doi:10.1080/01431160500114706.
37. Holmgren, J., Å. Persson, and U. Söderman. 2008. “Species Identification of Individual Trees by Combining High Resolution Lidar Data with Multi-Spectral Images.” International Journal of Remote Sensing 29: 1537–1552. doi:10.1080/01431160701736471.
38. Jensen, J. R., and D. C. Cowen. 1999. “Remote Sensing of Urban Suburban Infrastructure and Socio-Economic Attributes.” Photogrammetric Engineering and Remote Sensing 65: 611–622.
39. Jensen, J. R., and J. Im. 2007. “Remote Sensing Change Detection in Urban Environments.” Geo-Spatial Technologies in Urban Environments 7–31. doi:10.1007/978-3-540-69417-5_2.
40. Ji, M., and J. R. Jensen. 1999. “Effectiveness of Sub-Pixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery.” Geocarto International 14: 33–41. doi:10.1080/10106049908542126.
41. Karimi, Y., S. O. Prasher, R. M. Patel, and S. H. Kim. 2006. “Application of Support Vector Machine Technology for Weed and Nitrogen Stress Detection in Corn.” Computers and Electronics in Agriculture 51: 99–109. doi:10.1016/j.compag.2005.12.001.
42. Ke, Y., L. J. Quackenbush, and J. Im. 2010. “Synergistic Use of Quickbird Multispectral Imagery and LIDAR Data for Object-Based Forest Species Classification.” Remote Sensing of Environment 114: 1141–1154. doi:10.1016/j.rse.2010.01.002.
43. Khorram, S., J. A. Brockhaus, and H. M. Cheshire. 1987. “Comparson of Landsat MSS and TM Data for Urban Land-Use Classification.” IEEE Transactions on Geoscience and Remote Sensing GE-25: 238–243. doi:10.1109/TGRS.1987.289823.
44. Kim, Y., and Y. Kim. 2014. “Improved Classification Accuracy Based on the Output-Level Fusion of High-Resolution Satellite Images and Airborne LiDAR Data in Urban Area.” IEEE Geoscience and Remote Sensing Letters 11: 636–640. doi:10.1109/LGRS.2013.2273397.
45. Koetz, B., F. Morsdorf, S. Van Der Linden, T. Curt, and B. Allgöwer. 2008. “Multi-Source Land Cover Classification for Forest Fire Management Based on Imaging Spectrometry and Lidar Data.” Forest Ecology and Management 256: 263–271. doi:10.1016/j.foreco.2008.04.025.
46. Lee, D. S., and J. Shan. 2003. “Combining Lidar Elevation Data and IKONOS Multispectral Imagery for Coastal Classification Mapping.” Marine Geodesy 26: 117–127. doi:10.1080/01490410306707.
47. Lee, J. B., S. Woodyatt, and M. Berman. 1990. “Enhancement of High Spectral Resolution Remote Sensing Data by a Noise-Adjusted Principal Components Transform.” IEEE Transactions on Geoscience and Remote Sensing 28: 295–304. doi:10.1109/36.54356.
48. Li, S., H. Wu, D. Wan, and J. Zhu. 2011. “An Effective Feature Selection Method for Hyperspectral Image Classification Based on Genetic Algorithm and Support Vector Machine.” Knowledge-Based Systems 24: 40–48. doi:10.1016/j.knosys.2010.07.003.
49. Licciardi, G., F. Pacifici, D. Tuia, S. Prasad, T. West, F. Giacco, C. Thiel, J. Inglada, E. Christophe, J. Chanussot, and P. Gamba. 2009. “Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest.” IEEE Transactions on Geoscience and Remote Sensing 47: 3857–3865. doi:10.1109/TGRS.2009.2029340.
50. Liu, K., W. Shi, and H. Zhang. 2011. “A Fuzzy Topology-Based Maximum Likelihood Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 66: 103–114. doi:10.1016/j.isprsjprs.2010.09.007.
51. Macfaden, S. W., J. P. M. O’neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. “High-Resolution Tree Canopy Mapping for New York City Using LIDAR and Object-Based Image Analysis.” Journal of Applied Remote Sensing 6: 1–23. doi:10.1117/1.JRS.6.063567.
52. Madhok, V., and D. Landgrebe. 1999. “Supplementing Hyperspectral Data with Digital Elevation.” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 1999. IGARSS’99 1: 59–61. doi:10.1109/IGARSS.1999.773400.
53. Mangolini, M. 1994. “Apport de la fusion d’images satellitaires multicapteurs au niveau pixel en télédétection et photo-interprétation.” Dissertation published at the University of Nice-Sophia Antipolis, France.
54. Meng, X. L., L. Wang, J. L. Silván-Cárdenas, and N. Currit. 2009. “A Multi-Directional Ground Filtering Algorithm for Airborne LIDAR.” ISPRS Journal of Photogrammetry and Remote Sensing 64: 117–124. doi:10.1016/j.isprsjprs.2008.09.001.
55. Miliaresis, G., and N. Kokkas. 2007. “Segmentation and Object-Based Classification for the Extraction of the Building Class from LIDAR Dems.” Computers & Geosciences 33: 1076–1087. doi:10.1016/j.cageo.2006.11.012.
56. Mueller, M., K. Segl, and H. Kaufmann. 2004. “Edge- and Region-Based Segmentation Technique for the Extraction of Large, Man-Made Objects in High-Resolution Satellite Imagery.” Pattern Recognition 37 (8): 1619–1628. doi:10.1016/j.patcog.2004.03.001.
57. Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. 2011. “Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115: 1145–1161. doi:10.1016/j.rse.2010.12.017.
58. Pengra, B. W., C. A. Johnston, and T. R. Loveland. 2007. “Mapping an Invasive Plant, Phragmites Australis, in Coastal Wetlands Using the EO-1 Hyperion Hyperspectral Sensor.” Remote Sensing of Environment 108: 74–81. doi:10.1016/j.rse.2006.11.002.
59. Petropoulos, G. P., K. Arvanitis, and N. Sigrimis. 2012. “Hyperion Hyperspectral Imagery Analysis Combined with Machine Learning Classifiers for Land Use/Cover Mapping.” Expert Systems with Applications 39: 3800–3809. doi:10.1016/j.eswa.2011.09.083.
60. Petropoulos, G. P., C. Kalaitzidis, and K. Prasad Vadrevu. 2012. “Support Vector Machines and Object-Based Classification for Obtaining Land-Use/Cover Cartography from Hyperion Hyperspectral Imagery.” Computers & Geosciences 41: 99–107. doi:10.1016/j.cageo.2011.08.019.
61. Pignatti, S. R. M., R. M. Cavalli, V. Cuomo, L. Fusilli, S. Pascucci, M. Poscolieri, and F. Santini. 2009. “Evaluating Hyperion Capability for Land Cover Mapping in a Fragmented Ecosystem: Pollino National Park, Italy.” Remote Sensing of Environment 113: 622–634. doi:10.1016/j.rse.2008.11.006.
62. Pohl, C., and J. L. Van Genderen. 1994. “Multi-Sensor Fusion: Optimization and Operationalization for Mapping Applications.” SPIE’s International Symposium on Optical Engineering and Photonics in Aerospace Sensing 17–25. doi:10.1117/12.177750.
63. Pohl, C., and J. L. Van Genderen. 1998. “Review Article Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications.” International Journal of Remote Sensing 19: 823–854. doi:10.1080/014311698215748.
64. Pouliot, D. A., D. J. King, F. W. Bell, and D. G. Pitt. 2002. “Automated Tree Crown Detection and Delineation in High-Resolution Digital Camera Imagery of Coniferous Forest Regeneration.” Remote Sensing of Environment 82: 322–334. doi:10.1016/S0034-4257(02)00050-0.
65. Powell, R. L., D. A. Roberts, P. E. Dennison, and L. L. Hess. 2007. “Sub-Pixel Mapping of Urban Land Cover Using Multiple Endmember Spectral Mixture Analysis: Manaus, Brazil.” Remote Sensing of Environment 106: 253–267. doi:10.1016/j.rse.2006.09.005.
66. Pu, R., B. Xu, and P. Gong. 2003. “Oakwood Crown Closure Estimation by Unmixing Landsat TM Data.” International Journal of Remote Sensing 24: 4422–4445. doi:10.1080/0143116031000095989.
67. Rottensteiner, F., J. Trinder, S. Clode, and K. Kubik. 2005. “Using the Dempster–Shafer Method for the Fusion of LIDAR Data and Multi-Spectral Images for Building Detection.” Information Fusion 6: 283–300. doi:10.1016/j.inffus.2004.06.004.
68. Sasaki, T., J. Imanishi, K. Ioki, Y. Morimoto, and K. Kitada. 2012. “Object-Based Classification of Land Cover and Tree Species by Integrating Airborne Lidar and High Spatial Resolution Imagery Data.” Landscape and Ecological Engineering 8: 157–171. doi:10.1007/s11355-011-0158-z.
69. Scott, A. J., and M. J. Symons. 1971. “Clustering Methods Based on Likelihood Ratio Criteria.” Biometrics 27: 387–397. doi:10.2307/2529003.
70. Shackelford, A. K., and C. H. Davis. 2003. “A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multispectral Data over Urban Areas.” IEEE Transactions on Geoscience and Remote Sensing 41: 2354–2364. doi:10.1109/TGRS.2003.815972.
71. Singh, K. K., J. B. Vogler, D. A. Shoemaker, and R. K. Meentemeyer. 2012. “Lidar-Landsat Data Fusion for Large-Area Assessment of Urban Land Cover: Balancing Spatial Resolution, Data Volume and Mapping Accuracy.” ISPRS Journal of Photogrammetry and Remote Sensing 74: 110–121. doi:10.1016/j.isprsjprs.2012.09.009.
72. Small, C. 2001. “Estimation of Urban Vegetation Abundance by Spectral Mixture Analysis.” International Journal of Remote Sensing 22: 1305–1334. doi:10.1080/01431160151144369.
73. Small, C. 2003. “High Spatial Resolution Spectral Mixture Analysis of Urban Reflectance.” Remote Sensing of Environment 88: 170–186. doi:10.1016/j.rse.2003.04.008.
74. Small, C. 2005. “A Global Analysis of Urban Reflectance.” International Journal of Remote Sensing 26: 661–681. doi:10.1080/01431160310001654950.
75. Song, J. H., S. H. Han, K. Y. Yu, and Y. Kim. 2002. “Assessing the Possibility of Land-Cover Classification Using Lidar Intensity Data.” International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 34: 259–262.
76. Stehman, S. V. 1997. “Selecting and Interpreting Measures of Thematic Classification Accuracy.” Remote Sensing of Environment 62: 77–89. doi:10.1016/S0034-4257(97)00083-7.
77. Stow, D., A. Lopez, C. Lippitt, S. Hinton, and J. Weeks. 2007. “Object‐Based Classification of Residential Land Use within Accra, Ghana Based on Quickbird Satellite Data.” International Journal of Remote Sensing 28: 5167–5173. doi:10.1080/01431160701604703.
78. Sun, J., J. Yang, C. Zhang, W. Yun, and J. Qu. 2013. “Automatic Remotely Sensed Image Classification in a Grid Environment Based on the Maximum Likelihood Method.” Mathematical and Computer Modelling 58: 573–581. doi:10.1016/j.mcm.2011.10.063.
79. Tiwari, P. S., and H. Pande. 2008. “Use of Laser Range and Height Texture Cues for Building Identification.” Journal of the Indian Society of Remote Sensing 36: 227–234. doi:10.1007/s12524-008-0023-1.
80. Tóvári, D., and T. Vögtle. 2004. “Classification Methods for 3D Objects in Laser Scanning Data.” International Archives of Photogrammetry and Remote Sensing 35: 1682–1750.
81. Van Coillie, F., L. P. C. Verbeke, and R. R. De Wulf. 2007. “Feature Selection by Genetic Algorithms in Object-Based Classification of IKONOS Imagery for Forest Mapping in Flanders, Belgium.” Remote Sensing of Environment 110: 476–487. doi:10.1016/j.rse.2007.03.020.
82. Wang, Z., W. Wei, S. Zhao, and X. Chen. 2004. “Object-Oriented Classification and Application in Land Use Classification Using SPOT-5 PAN Imagery.” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS’04 5: 3158–3160. doi:10.1109/IGARSS.2004.1370370.
83. Weidner, U., and W. Förstner. 1995. “Towards Automatic Building Extraction from High Resolution Digital Elevation Models.” ISPRS Journal of Photogrammetry and Remote Sensing 50: 38–49. doi:10.1016/0924-2716(95)98236-S.
84. Wu, B., Z. Xiong, Y. Chen, and Y. Zhao. 2009. “Classification of Quickbird Image with Maximal Mutual Information Feature Selection and Support Vector Machine.” Procedia Earth and Planetary Science 1: 1165–1172. doi:10.1016/j.proeps.2009.09.179.
85. Xu, D., G. Ni, L. Jiang, Y. Shen, T. Li, S. Ge, and X. Shu. 2008. “Exploring for Natural Gas Using Reflectance Spectra of Surface Soils.” Advances in Space Research 41: 1800–1817. doi:10.1016/j.asr.2007.05.073.
86. Zhang, R., and J. Ma. 2009. “Feature Selection for Hyperspectral Data Based on Recursive Support Vector Machines.” International Journal of Remote Sensing 30: 3669–3677. doi:10.1080/01431160802609718.
87. Zhou, W. 2013. “An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data.” IEEE Geoscience and Remote Sensing Letters 10: 928–931. doi:10.1109/LGRS.2013.2251453.