1,118
Views
52
CrossRef citations to date
0
Altmetric
Original Articles

Object-based classification with rotation forest ensemble learning algorithm using very-high-resolution WorldView-2 image

, &
Pages 834-843 | Received 29 May 2015, Accepted 12 Aug 2015, Published online: 07 Sep 2015

References

  • Baatz, M., and A. Schape. 2000. “Multi Resolution Segmentation: An Optimization Approach for High Quality Multi Scale Image Segmentation.” In Angewandte Geographische Informations Verarbeitung XII, edited by J. Strobl, T. Blaschke, and G. Greisebener, 12−23. Karlsruhe: Herbert Wichmann Verlag.
  • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Drăguţ, L., D. Tiede, and S. R. Levick. 2010. “ESP: A Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data.” International Journal of Geographical Information Science 24 (6): 859–871. doi:10.1080/13658810903174803.
  • Du, P., A. Samat, B. Waske, S. Liu, and Z. Li. 2015. “Random Forest and Rotation Forest for Fully Polarized SAR Image Classification Using Polarimetric and Spatial Features.” ISPRS Journal of Photogrammetry and Remote Sensing 105: 38–53. doi:10.1016/j.isprsjprs.2015.03.002.
  • Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
  • Japkowicz, N., and M. Shah. 2011. Evaluating Learning Algorithms. New York: Cambridge University Press.
  • Kavzoglu, T., and I. Colkesen. 2009. “A Kernel Functions Analysis for Support Vector Machines for Land Cover Classification.” International Journal of Applied Earth Observation and Geoinformation 11 (5): 352–359. doi:10.1016/j.jag.2009.06.002.
  • Kavzoglu, T., and I. Colkesen. 2013. “An Assessment of the Effectiveness of a Rotation Forest Ensemble for Land-Use and Land-Cover Mapping.” International Journal of Remote Sensing 34 (12): 4224–4241. doi:10.1080/01431161.2013.774099.
  • Kavzoglu, T., and M. Yildiz. 2014. “Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and QuikBird-2 Images.” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-7: 31–37. doi:10.5194/isprsannals-II-7-31-2014.
  • Kim, M., T. A. Warner, M. Madden, and D. S. Atkinson. 2011. “Multi-Scale GEOBIA with Very High Spatial Resolution Digital Aerial Imagery: Scale, Texture and Image Objects.” International Journal of Remote Sensing 32 (10): 2825–2850. doi:10.1080/01431161003745608.
  • Kuncheva, L. I., and J. J. Rodríguez. 2007. “An Experimental Study on Rotation Forest Ensembles.” In Multiple Classifier Systems, edited by M. Haindl, J. Kittler, and F. Roli, 459–468. Berlin: Springer.
  • Liu, K.-H., and D.-S. Huang. 2008. “Cancer Classification Using Rotation Forest.” Computers in Biology and Medicine 38 (5): 601–610. doi:10.1016/j.compbiomed.2008.02.007.
  • Löw, F., U. Michel, S. Dech, and C. Conrad. 2013. “Impact of Feature Selection on the Accuracy and Spatial Uncertainty of Per-Field Crop Classification Using Support Vector Machines.” ISPRS Journal of Photogrammetry and Remote Sensing 85: 102–119. doi:10.1016/j.isprsjprs.2013.08.007.
  • Ma, L., L. Cheng, M. C. Li, Y. X. Liu, and X. X. Ma. 2015. “Training Set Size, Scale, and Features in Geographic Object-Based Image Analysis of Very High Resolution Unmanned Aerial Vehicle Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 102: 14–27. doi:10.1016/j.isprsjprs.2014.12.026.
  • Maxwell, A. E., T. A. Warner, M. P. Strager, J. F. Conley, and A. L. Sharp. 2015. “Assessing Machine-learning Algorithms and Image- and Lidar-derived Variables for GEOBIA Classification of Mining and Mine Reclamation.” International Journal of Remote Sensing 36 (4): 954–978. doi:10.1080/01431161.2014.1001086.
  • Maxwell, A. E., T. A. Warner, M. P. Strager, and M. Pal. 2014. “Combining Rapid Eye Satellite Imagery and Lidar for Mapping of Mining and Mine Reclamation.” Photogrammetric Engineering & Remote Sensing 80 (2): 179–189. doi:10.14358/PERS.80.2.179-189.
  • Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2297–2307. doi:10.1109/TGRS.2009.2039484.
  • Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-based Extreme Learning Machine for Remote-sensing Image Classification.” Remote Sensing Letters 4 (9): 853–862. doi:10.1080/2150704X.2013.805279.
  • Qian, Y. G., W. Q. Zhou, J. L. Yan, W. F. Li, and L. J. Han. 2015. “Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery.” Remote Sensing 7 (1): 153–168. doi:10.3390/rs70100153.
  • Rodriguez, J. J., L. I. Kuncheva, and S. J. Alanso. 2006. “Rotation Forest: A New Classifier Ensemble Method.” IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (10): 1619–1630. doi:10.1109/TPAMI.2006.211.
  • Sellers, P. J., B. W. Meeson, F. G. Hall, G. Asrar, R. E. Murphy, R. A. Schiffer, and F. P. Bretherton et al. 1995. “Remote-Sensing of the Land-Surface for Studies of Global Change; Models, Algorithms, Experiments.” Remote Sensing of Environment 51 (1): 3–26. doi:10.1016/0034-4257(94)00061-Q.
  • Tzotsos, A., K. Karantzalos, and D. Argialas. 2011. “Object-based Image Analysis through Nonlinear Scale-Space Filtering.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (1): 2–16. doi:10.1016/j.isprsjprs.2010.07.001.
  • Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Wieland, M., and M. Pittore. 2014. “Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images.” Remote Sensing 6 (4): 2912–2939. doi:10.3390/rs6042912.
  • Xia, J. S., P. J. Du, X. Y. He, and J. Chanussot. 2014. “Hyperspectral Remote Sensing Image Classification Based on Rotation Forest.” IEEE Geoscience and Remote Sensing Letters 11 (1): 239–243. doi:10.1109/LGRS.2013.2254108.
  • Zhang, C.-X., and J.-S. Zhang. 2010. “A Variant of Rotation Forest for Constructing Ensemble Classifiers.” Pattern Analysis and Applications 13 (1): 59–77. doi:10.1007/s10044-009-0168-8.
  • Zhang, C.-X., J.-S. Zhang, and G.-W. Wang. 2008. “An Empirical Study of Using Rotation Forest to Improve Regressors.” Applied Mathematics and Computation 195 (2): 618–629. doi:10.1016/j.amc.2007.05.010.

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.