658
Views
23
CrossRef citations to date
0
Altmetric
Articles

A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

, , , &
Pages 7176-7195 | Received 24 Mar 2017, Accepted 19 Aug 2017, Published online: 31 Aug 2017

References

  • Agarwal, S., L. S. Vailshery, M. Jaganmohan, and H. Nagendra. 2013. “Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches.” ISPRS International Journal of Geo-Information 2 (1): 220–236. doi:10.3390/ijgi2010220.
  • Antonarakis, A. S., K. S. Richards, and J. Brasington. 2008. “Object-Based Land Cover Classification Using Airborne LiDAR.” Remote Sensing of Environment 112 (6): 2988–2998. doi:10.1016/j.rse.2008.02.004.
  • Bartholomé, E., and A. Belward. 2005. “GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data.” International Journal of Remote Sensing 26: 1959–1977. doi:10.1080/01431160412331291297.
  • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry & Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
  • 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.” Photogrammetric Record 27 (140): 401–422. doi:10.1111/j.1477-9730.2012.00698.x.
  • Chang, C. C., and C. J. Lin. 2007. “LIBSVM: A Library for Support Vector Machines.” Acm Transactions on Intelligent Systems & Technology 2 (3,article 27): 389–396.
  • 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 (7): 1101–1110. doi:10.1016/j.asr.2008.11.008.
  • Chen, Z., and B. Gao. 2014. “An Object-Based Method for Urban Land Cover Classification Using Airborne LiDAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 7 (10): 4243–4254. doi:10.1109/JSTARS.2014.2332337.
  • Cleve, C., M. Kelly, F. R. Kearns, and M. Moritz. 2008. “Classification of the Wild Land-Urban Interface: A Comparison of Pixel- and Object-Based Classifications Using High-Resolution Aerial Photography.” Computers Environment & Urban Systems 32 (4): 317–326. doi:10.1016/j.compenvurbsys.2007.10.001.
  • Colditz, R. 2015. “An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms.” Remote Sensing 7 (8): 9655–9681. doi:10.3390/rs70809655.
  • Duda, R. O., and P. E. Hart. 1973. Pattern Classification and Scene Analysis. New York: Wiley.
  • Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118 (6): 259–272. doi:10.1016/j.rse.2011.11.020.
  • Friedl, M., D. McIver, J. Hodges, X. Zhang, D. Muchoney, A. Strahler, A. Cooper, A. Baccini, F. Gao, and C. Schaaf. 2002. “Global Land Cover Mapping from MODIS: Algorithms and Early Results.” Remote Sensing of Environment 83: 287–302. doi:10.1016/S0034-4257(02)00078-0.
  • Fu, B., Y. Wang, A. Campbell, Y. Li, B. Zhang, S. Yin, Z. F. Xing, and X. M. Jin. 2017. “Comparison of Object-Based and Pixel-Based Random Forest Algorithm for Wetland Vegetation Mapping Using High Spatial Resolution GF-1 and SAR Data.” Ecological Indicators 73: 105–117. doi:10.1016/j.ecolind.2016.09.029.
  • Guan, H., Z. Ji, L. Zhong, J. Li, and Q. Ren. 2013. “Partially Supervised Hierarchical Classification for Urban Features from LiDAR Data with Aerial Imagery.” International Journal of Remote Sensing 34: 190–210. doi:10.1080/01431161.2012.712228.
  • ISPRS. 2013. Web Site of the ISPRS Test Project on Urban Classification and 3D Building Reconstruction.  Accessed 15 January 2015.  http://www2.isprs.org/commissions/comm3/wg4/results.html.
  • Jin, X. Y., 2009. Segmentation-Based Image Processing System. US Patent 20090123070, filed November 14.
  • Kaszta, Z., R. V. D. Kerchove, A. Ramoelo, M. A. Cho, S. Madonsela, R. Mathieu, and E. Wolff. 2016. “Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel and Object-Based Approaches and Selected Classification Algorithms.” Remote Sensing 8 (9): 763. doi:10.3390/rs8090763.
  • Kolios, S., and C. Stylios. 2013. “Identification of Land Cover/Land Use Changes in the Greater Area of the Preveza Peninsula in Greece Using Landsat Satellite Data.” Applied Geography 40: 150–160. doi:10.1016/j.apgeog.2013.02.005.
  • Kong, F. J., X. B. Li, H. Wang, D. F. Xie, X. Li, and Y. X. Bai. 2016. “Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series.” Remote Sensing 8 (9): 741. doi:10.3390/rs8090741.
  • Loveland, T., B. Reed, J. Brown, D. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant. 2000. “Development of a Global Land Cover Characteristics Database and IGBP Discover from 1 Km AVHRR Data.” International Journal of Remote Sensing 21: 1303–1330. doi:10.1080/014311600210191.
  • Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28 (5): 823–870. doi:10.1080/01431160600746456.
  • 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 (5): 1145–1161. doi:10.1016/j.rse.2010.12.017.
  • Otukei, J. R., T. Blaschke, T. Woldai, and H. Annegarn. 2010. “Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms.” International Journal of Applied Earth Observation & Geoinformation 12 (1): S27–S31. doi:10.1016/j.jag.2009.11.002.
  • Petropoulos, P. G., C. Kontoes, and I. Keramitsoglou. 2011. “Burnt Area Delineation from a Uni-Temporal Perspective Based on Landsat TM Imagery Classification Using Support Vector Machines.” International Journal of Applied Earth Observation and Geoinformation 13: 70–80. doi:10.1016/j.jag.2010.06.008.
  • Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers.
  • Räsänen, A., M. Kuitunen, E. Tomppo, and A. Lensu. 2014. “Coupling High-Resolution Satellite Imagery with ALS-based Canopy Height Model and Digital Elevation Model in Object-Based Boreal Forest Habitat Type Classification.” ISPRS Journal of Photogrammetry & Remote Sensing 94 (8): 169–182. doi:10.1016/j.isprsjprs.2014.05.003.
  • Rittl, T., M. Cooper, R. J. Heck, and M. V. R. Ballester. 2013. “Object-Based Method Outperforms Per-Pixel Method for Land Cover Classification in a Protected Area of the Brazilian Atlantic Rainforest Region.” Pedosphere 23 (3): 290–297. doi:10.1016/S1002-0160(13)60018-1.
  • Robinson, D. J., N. J. Redding, and D. J. Crisp. 2002. Implementation of a Fast Algorithm for Segmenting SAR Imagery. Australia: Defense Science and Technology Organization.
  • Rottensteiner, F., G. Sohn, M. Gerke, J. D. Wegner, U. Breitkopf, and J. Jung. 2014. “Results of the ISPRS Benchmark on Urban Object Detection and 3D Building Reconstruction.” ISPRS Journal of Photogrammetry and Remote Sensing 93: 256–271. doi:10.1016/j.isprsjprs.2013.10.004.
  • 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 Ecology Engineering 8: 157–171. doi:10.1007/s11355-011-0158-z.
  • Secord, J., and A. Zakhor. 2007. “Tree Detection in Urban Regions Using Aerial LiDAR and Image Data.” IEEE Geoscience & Remote Sensing Letters 4 (2): 196–200. doi:10.1109/LGRS.2006.888107.
  • Szantoi, Z., F. Escobedo, A. Abd-Elrahman, S. Smith, and L. Pearlstine. 2013. “Analyzing Fine-Scale Wetland Composition Using High Resolution Imagery and Texture Features.” International Journal of Applied Earth Observation & Geoinformation 23 (8): 204–212. doi:10.1016/j.jag.2013.01.003.
  • Szantoi, Z., F. J. Escobedo, A. Abd-Elrahman, L. Pearlstine, B. Dewitt, and S. Smith. 2015. “Classifying Spatially Heterogeneous Wetland Communities Using Machine Learning Algorithms and Spectral and Textural Features.” Environmental Monitoring & Assessment 187 (5): 1–15. doi:10.1007/s10661-015-4426-5.
  • Tucker, C., D. Grant, and J. Dykstra. 2004. “NASA’s Global Orthorectified Landsat Data Set.” Photogrammetric Engineering & Remote Sensing 70: 313–322. doi:10.14358/PERS.70.3.313.
  • Whiteside, T. G., G. S. Boggs, and S. W. Maier. 2011. “Comparing Object-Based and Pixel-Based Classifications for Mapping Savannas.” International Journal of Applied Earth Observation & Geoinformation 13 (6): 884–893. doi:10.1016/j.jag.2011.06.008.
  • Wu, W., R. Shibasaki, P. Yang, L. Ongaro, Q. Zhou, and H. Tang. 2008. “Validation and Comparison of 1 Km Global Land Cover Products in China.” International Journal of Remote Sensing 29 (13): 3769–3785. doi:10.1080/01431160701881897.
  • Yan, W. Y., A. Shaker, and N. El-Ashmawy. 2015. “Urban Land Cover Classification Using Airborne LiDAR Data: A Review.” Remote Sensing of Environment 158: 295–310. doi:10.1016/j.rse.2014.11.001.
  • Zhang, J., X. Lin, and X. Ning. 2013. “SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas.” Remote Sensing 5 (8): 3749–3775. doi:10.3390/rs5083749.
  • Zhang, X., and S. Du. 2016. “Learning Selfhood Scales for Urban Land Cover Mapping with Very-High-Resolution Satellite Images.” Remote Sensing of Environment 178: 172–190. doi:10.1016/j.rse.2016.03.015.
  • Zhou, W. Q. 2013. “An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data.” IEEE Geoscience & Remote Sensing Letters 10 (4): 928–931. doi:10.1109/LGRS.2013.2251453.
  • Zhu, X., and T. Toutin. 2013. “Land Cover Classification Using Airborne LiDAR Products in Beauport, Québec, Canada.” International Journal of Image and Data Fusion 4: 252–271. doi:10.1080/19479832.2012.734339.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.