An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping

International Journal of Remote Sensing

Volumn 34, Issue 12, 2013, Pages 4224-4241

  Kavzoglu, Taskin ^a   Colkesen, Ismail ^a  

^a GEBZE TECHNICAL UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FORESTRY; LAND USE; MAPS; PROBABILITY DISTRIBUTIONS; REMOTE SENSING; ROTATION;

CHI-SQUARED DISTRIBUTION; CLASSIFICATION ACCURACY; CLASSIFIER PERFORMANCE; INDIVIDUAL CLASSIFIERS; LAND USE AND LAND COVER; LAND USE AND LAND COVER MAPPING; RANDOM SUBSPACE METHOD; STATISTICAL SIGNIFICANCE;

MAPPING;

FORESTRY; IMAGE CLASSIFICATION; LAND COVER; LAND USE; REMOTE SENSING; SATELLITE DATA; THEMATIC MAPPING; ACCURACY ASSESSMENT; FOREST DYNAMICS; PREDICTION;

CLASSIFICATION; FORESTRY; IMAGE ANALYSIS; INFORMATION RETRIEVAL; LAND USE; MAPPING; MAPS; ROTATION;

EID: 84875922650     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2013.774099     Document Type: Article

References (61)

1. Agresti, A. 1996. An Introduction to Categorical Data Analysis, New York: Wiley.
2. Banfield, R. E., Hall, L. O., Bowyer, K. W. and Kegelmeyer, W. P. 2007. A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29: 173-180.
3. Ben-David, A. 2008. Comparison of Classification Accuracy Using Cohen's Weighted Kappa. Expert Systems with Applications, 34: 825-832.
4. Boulila, W., Farah, I. R., Ettabaa, K. S. and Solaiman, B. 2011. A Data Mining Based Approach to Predict Spatiotemporal Changes in Satellite Images. International Journal of Applied Earth Observation and Geoinformation, 13: 386-395. H. Ben Ghezala""
5. Breiman, L. 1996. Bagging Predictors. Machine Learning, 24: 123-140.
6. Breiman, L. 1998. Arcing Classifiers. Annals of Statistics, 26: 801-824.
7. Breiman, L. 2001. Random Forests. Machine Learning, 45: 5-32.
8. Congalton, R. G. 1991. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37: 35-46.
9. Congalton, R. G. and Green, K. 2009. Assessing the Accuracy of Remotely Sensed Data, Boca Raton, FL: CRC.
10. De Leeuw, J., Jia, H., Yang, L., Liu, X., Schmidt, K. and Skidmore, A. K. 2006. Comparing Accuracy Assessments to Infer Superiority of Image Classification Methods. International Journal of Remote Sensing, 27: 223-232.
11. Dietterich, T. G. 1998. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10: 1895-1923.
12. Dietterich, T. G. 2000. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning, 40: 139-157.
13. Duda, R. O., Hart, P. E. and Stork, D. G. 2000. Pattern Classification, New York: John Wiley and Sons.
14. Ferri, C., Hernandez-Orallo, J. and Modroiu, R. 2009. An Experimental Comparison of Performance Measures for Classification. Pattern Recognition Letters, 30: 27-38.
15. Fleiss, J. L., Levin, B. and Paik, M. C. 2003. Statistical Methods for Rates and Proportions, New Jersey: Wiley.
16. Foody, G. M. 1992. On the Compensation for Chance Agreement in Image Classification Accuracy Assessment. Photogrammetric Engineering and Remote Sensing, 58: 1459-1460.
17. Foody, G. M. 2002. Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, 80: 185-201.
18. Foody, G. M. 2004. Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 70: 627-633.
19. Foody, G. M. 2008. Harshness in Image Classification Accuracy Assessment. International Journal of Remote Sensing, 29: 3137-3158.
20. Foody, G. M. 2009. Classification Accuracy Comparison: Hypothesis Tests and the Use of Confidence Intervals in Evaluations of Difference, Equivalence and Non-Inferiority. Remote Sensing of Environment, 113: 1658
21. Freund, Y. and Scapire, R. 1996. "Experiments with a New Boosting Algorithm 148-156. Francisco, Italy 3-6 July, Bari andIn 13th International Conference on Machine Learning, edited by L. SaittaSan CA: Morgan Kaufmann
22. Friedl, M. A. and Brodley, C. E. 1997. Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment, 61: 399-409.
23. Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. and Huang, X. M. 2010. MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets. Remote Sensing of Environment, 114: 168-182.
24. Gao, Y., Mas, J. F. and Kerle, N. 2011. Optimal Region Growing Segmentation and Its Effect on Classification Accuracy. International Journal of Remote Sensing, 32: 3747-3763. J. A. N. Pacheco""
25. Gao, Y., Mas, J. F., Maathui{dotless}s, B. H. P. and Zhang, X. M. 2006. Comparison of Pixel-Based and Object-Oriented Image Classification Approaches-a Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27: 4039-4055. P. M. Van Di{dotless}jk""
26. Gislason, P. O., Benediktsson, J. A. and Sveinsson, J. R. 2004. Random Forest Classification of Multisource Remote Sensing and Geographic Data 1049-1052. "" In IEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS 2004), Anchorage, AK, September 20-24, vol. II, Hoboken, NJ: IEEE International
27. Gislason, P. O., Benediktsson, J. A. and Sveinsson, J. R. 2006. Random Forests for Land Cover Classification. Pattern Recognition Letters, 27: 294-300.
28. Hamza, M. and Larocque, D. 2005. An Empirical Comparison of Ensemble Methods Based on Classification Trees. Journal of Statistical Computation and Simulation, 75: 629-643.
29. Han, M., Zhu, X. R. and Yao, W. 2012. Remote Sensing Image Classification Based on Neural Network Ensemble Algorithm. Neurocomputing, 78: 133-138.
30. Ho, T. K. 1998. The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20: 832-844.
31. Kavzoglu, T. 2009. Increasing the Accuracy of Neural Network Classification Using Refined Training Data. Environmental Modelling & Software, 24: 850-858.
32. Kim, Y. S. 2009. Boosting and Measuring the Performance of Ensembles for Successful Database Marketing. Expert Systems with Applications, 36: 2161-2176.
33. Kuncheva, L. I. and Whitaker, C. J. 2003. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning, 51: 181-207.
34. Liu, C. R., Frazier, P. and Kumar, L. 2007. Comparative Assessment of the Measures of Thematic Classification Accuracy. Remote Sensing of Environment, 107: 606-616.
35. Liu, K. H. and Huang, D. S. 2008. Cancer Classification Using Rotation Forest. Computers in Biology and Medicine, 38: 601-610.
36. Lu, D. and Weng, Q. 2007. A Survey of Image Classification Methods and Techniques for Improving Classification Performance. International Journal of Remote Sensing, 28: 823-870.
37. Melville, P. 2003. "Creating Diverse Ensemble Classifiers". In Technical Report UT-AI-TR-03-306, Artificial Intelligence Lab, University of Texas, Austin.
38. Melville, P. and Mooney, R. J. 2005. Creating Diversity in Ensembles Using Artificial Data. Information Fusion, 6: 99-111.
39. Nanni, L. and Lumini, A. 2006. An Experimental Comparison of Ensemble of Classifiers for Biometric Data. Neurocomputing, 69: 1670-1673.
40. Pal, M. 2005. Random Forest Classifier for Remote Sensing Classification. International Journal of Remote Sensing, 26: 217-222.
41. Pal, M. 2007. Ensemble Learning with Decision Tree for Remote Sensing Classification. World Academy of Science, Engineering and Technology, 36: 258-260.
42. Pal, M. 2008. Artificial Immune-Based Supervised Classifier for Land-Cover Classification. International Journal of Remote Sensing, 29: 2273-2291.
43. Pal, M. and Foody, G. M. 2010. Feature Selection for Classification of Hyperspectral Data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48: 2297-2307.
44. Pal, M. and Mather, P. M. 2003. An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification. Remote Sensing of Environment, 86: 554-565.
45. Pontius, R. G., and M. Millones. 2008. "Problems and Solutions for Kappa-Based Indices of Agreement." Paper presented at the International Conference on Studying, Modeling and Sense Making of Planet Earth, University of the Aegean, Mytilene, June 1-6.
46. Rodriguez, J. J., Kuncheva, L. I. and Carlos, J. 2006. Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28: 1619
47. Salzberg, S. L. 1997. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery, 1: 317-327.
48. Stehman, S. V. 1997. Selecting and Interpreting Measures of Thematic Classification Accuracy. Remote Sensing of Environment, 62: 77-89.
49. Stiglic, G. and Kokol, P. 2007. "Effectiveness of Rotation Forest in Meta-Learning based Gene Expression Classification". In Paper presented at the 20th IEEE International Symposium on Computer-Based Medical Systems Washington "" Maribor, June 20-22, 243-250. DC: IEEE Computer Society
50. Stiglic, G., Rodriguez, J. J. and Kokol, P. 2011. Rotation of Random Forests for Genomic and Proteomic Classification Problems. Software Tools and Algorithms for Biological Systems, 696: 211-221.
51. Sun, S. L., Zhang, C. S. and Zhang, D. 2007. An Experimental Evaluation of Ensemble Methods for EEG Signal Classification. Pattern Recognition Letters, 28: 2157-2163.
52. Tso, B. and Mather, P. M. 2009. Classification Methods for Remotely Sensed Data, 2nd, London: Taylor & Francis.
53. Wang, S. J., Mathew, A., Chen, Y., Xi, L. F., Ma, L. and Lee, J. 2009. Empirical Analysis of Support Vector Machine Ensemble Classifiers. Expert Systems with Applications, 36: 6466-6476.
54. Wang, Y. Y. and Li, J. 2008. Feature-Selection Ability of the Decision-Tree Algorithm and the Impact of Feature-Selection/Extraction on Decision-Tree Results Based on Hyperspectral Data. International Journal of Remote Sensing, 29: 2993-3010.
55. Webb, G. I. 2000. MultiBoosting: A Technique for Combining Boosting and Bagging. Machine Learning, 40: 159-196.
56. Webb, G. I. and Zheng, Z. J. 2004. Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques. IEEE Transactions on Knowledge and Data Engineering, 16: 980-991.
57. Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA: Morgan Kaufmann.
58. Xie, Y. C., Sha, Z. Y. and Yu, M. 2008. Remote Sensing Imagery in Vegetation Mapping: A Review. Journal of Plant Ecology, 1: 9-23.
59. Yan, W. Y. and Shaker, A. 2011. The Effects of Combining Classifiers with the Same Training Statistics Using Bayesian Decision Rules. International Journal of Remote Sensing, 32: 3729-3745.
60. Zhang, C. X. and Zhang, J. S. 2010. A Variant of Rotation Forest for Constructing Ensemble Classifiers. Pattern Analysis and Applications, 13: 59-77.
61. Zhang, C. X., Zhang, J. S. and Wang, G. W. 2008. An Empirical Study of Using Rotation Forest to Improve Regressors. Applied Mathematics and Computation, 195: 618-629.