Remote sensing image classification based on neural network ensemble algorithm

Neurocomputing

Volumn 78, Issue 1, 2012, Pages 133-138

  Han, Min ^a   Zhu, Xinrong ^a   Yao, Wei ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

DECORATE; Hybrid algorithm; RBFNN; Rotation Forest

Indexed keywords

ARTIFICIAL TRAINING; BASE CLASSIFIERS; CLASSIFICATION ERRORS; DATA SETS; DECORATE; ENSEMBLE ALGORITHMS; HYBRID ALGORITHMS; INTERPOLATION TECHNOLOGY; NEURAL NETWORK ENSEMBLES; RBF NEURAL NETWORK; RBFNN; RELABELING; REMOTE SENSING DATA; REMOTE SENSING IMAGE CLASSIFICATION; REMOTE SENSING IMAGES;

ALGORITHMS; FORESTRY; IMAGE RECONSTRUCTION; NEURAL NETWORKS; REMOTE SENSING; ROTATION;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CLASSIFIER; CONTROLLED STUDY; DIVERSITY ENSEMBLE CREATION BY OPPOSITIONAL RELABELING OF ARTIFICIAL TRAINING EXAMPLES; ERROR; KAPPA STATISTICS; KERNEL METHOD; MARSH; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; RADIAL BASED FUNCTION; RANDOM FOREST; REMOTE SENSING; ROTATION; ROTATION FOREST; WETLAND;

ALGORITHMS; FORESTRY; FORESTS; NEURAL NETWORKS; REMOTE SENSING; ROTATION;

EID: 82655173888     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.04.044     Document Type: Article

References (21)

1. Chen C.H., Peter Ho P.-G. Statistical pattern recognition in remote sensing. Pattern Recognition 2008, 41:2731-2741.
2. Yu E.L., Suganthan P.N. Ensemble of niching algorithms. Inf. Sci. 2010, 180:2815-2833.
3. Dos Santos E.M., Sabourin R., Maupin P. Overfitting cautious selection of classifier ensembles with genetic algorithms. Inf. Fusion 2009, 10:150-162.
4. De Bock K.W., Coussement K., Van den Poel D. Ensemble classification based on generalized additive models. Comput. Stat. Data Anal. 2010, 54:1535-1546.
5. Zhou Z.H., Wu J., Tang W. Ensembling neural networks: many could be better than all. Artif. Intell. 2002, 137:239-263.
6. Vilalta R., Drissi Y. A perspective view and survey of meta-learning. Artif. Intell. Rev. 2002, 18:77-95.
7. W.W. Cohen, V.R. Carvalho, Stacked sequential learning, in: Proceedings of the 19th International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers Inc., Edinburgh, Scotland, 2005, pp. 671-676.
8. A. Krogh, J. Vedelsby, Neural network ensembles, cross validation, and active learning, in: Advances in Neural Information Processing Systems, Denver, CO, USA, 1995, pp. 231-238.
9. Bae M.H., Wu T., Pan R. Mix-ratio sampling: classifying multiclass imbalanced mouse brain images using support vector machine. Expert Syst. Appl. 2010, 37:4955-4965.
10. Chawla N., Bowyer K., Hall L., Kegelmeyer P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002, 16:321-357.
11. P. Melville, R.J. Mooney, Constructing diverse classifier ensembles using artificial training examples, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, 2003, pp. 505-510.
12. Friedl M.A., Brodley C.E. Decision tree classification of land cover from remotely sensed data. Remote Sensing Environ. 1997, 61:399-409.
13. Liu K.H., Huang D.S. Cancer classification using rotation forest. Comput. Biol. Med. 2008, 38:601-610.
14. C.J. Blake, C.J. Merz, UCI Repository of Machine Learning Databases, 1998. http://www.ics.uci.edu.
15. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
16. Kuncheva L.I., Skurichina M., Duin R.P.W. An experimental study on diversity for bagging and boosting with linear classifiers. Inf. Fusion 2002, 3:245-258.
17. Tian J., Li M., Chen F. Dual-population based coevolutionary algorithm for designing RBFNN with feature selection. Expert Syst. Appl. 2010, 37:6904-6918.
18. Zhang C.X., Zhang J.S., Wang G.W. An empirical study of using rotation forest to improve regressors. Appl. Math. Comput. 2008, 195:618-629.
19. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
20. Quinlan J.R. C4. 5: Programs for Machine Learning 1993, Morgan Kaufmann, San Francisco, CA, USA.
21. Youn E., Jeong M.K. Class dependent feature scaling method using naive Bayes classifier for text datamining. Pattern Recognition Lett. 2009, 30:477-485.