Artificial neural network ensemble for land cover classification

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Volumn 2, Issue , 2006, Pages 10054-10057

  Lingmin, He ^a   Fansheng, Kong ^a   Zhangquan, Shen ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Artificial neural network; Ensemble; Land cover classification; Multisource

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; LANDFORMS; MAXIMUM LIKELIHOOD ESTIMATION; REMOTE SENSING; STATISTICAL METHODS;

ENSEMBLE; LAND COVER CLASSIFICATION; MULTISOURCE;

NEURAL NETWORKS;

EID: 34047227551     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WCICA.2006.1713966     Document Type: Conference Paper

References (11)

1. G. M. Foody, "Status of land cover classification accuracy assessment," Remote Sensing of Environment, vol. 80, pp. 185-201, 2002.
2. Eiumnoh A. and Shresta R. P. "Can DEM enhance the digital image classification?" Proceedings of Asian Conference on Remote Sensing, 1997.
3. Bektas, F., "Remote sensing and geographic information integration: A case study; Bozcaada & Gokceada Island," Msc Thesis, Institution of Science and Technology, Istanbul Technical University, 2003.
4. Harris, P. M., and Ventura, S. J., "The integration of geographic data with remotely sensed imagery to improve classification in an urban area," Photogrametric Engineering and Remote Sensing, vol. 61, pp. 993-998, 1995.
5. I. Kanellopoulos and G. G. Wilkinson, "Strategies and best practice for neural network image classification", Int. J. Remote Sensing, vol. 18, pp. 711-725, 1997.
6. C. M. Liu, L. J. Zhang, C. J. Davis, D. S. Solomon, et al, "Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data", Forest Sci., vol. 49, pp. 619-631, 2003.
7. Hansen, L.K., Salamon, P. "Neural network ensembles," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, No. 10993-1001, 1990.
8. Zhou, Z.H., Wu, J.X. et al, "Ensembling neural networks: Many could be better than all," Artificial Intelligence, vol. 137 pp. 239-263, 2002.
9. L. Breiman, "Bagging predictors," Machine Learning vol. 24, p. 123-140, 1996.
10. Schapire, R.E., "A brief introduction to boosting", In: Thomas, D. (ed.): Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999.
11. The mathworks, Neural network toolbox user's guide (version 4), the mathworks, Inc., Natick, Massachussets, 2001.