Classification of hyperspectral remote sensing image based on genetic algorithm and SVM

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7809, Issue , 2010, Pages

  Zhou, Mandi ^a   Shu, Jiong ^a   Chen, Zhigang ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Classification; Genetic algorithm; Hyperspectral; SVM

Indexed keywords

ACCURACY COMPARISONS; BAYESIAN ALGORITHMS; CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFIER MODELS; DECISION DIRECTED ACYCLIC GRAPHS; GRID-SEARCH; HIGH RESOLUTION; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL REMOTE SENSING DATA; HYPERSPECTRAL REMOTE SENSING IMAGE; HYPERSPECTRAL SENSORS; KERNEL FUNCTION; LAND COVER; MULTI-CLASS CLASSIFICATION; MULTI-CLASS PROBLEMS; NEURAL NETWORK MODEL; OPTIMAL PARAMETER; RADIAL BASIS FUNCTIONS; SHANGHAI INSTITUTE OF TECHNICAL PHYSICS; SPECTRAL SIGNATURE; STATISTICAL LEARNING THEORY; STRUCTURAL RISK MINIMIZATION; SVM; SVM CLASSIFICATION; SVM MODEL; TERRAIN CLASSIFICATION; TRAINING SAMPLE; URBAN DEVELOPMENT; URBAN VEGETATION; URGENT PROBLEMS; VEGETATION CLASSIFICATION;

CLASSIFIERS; ECOSYSTEMS; IMAGE RECONSTRUCTION; LANDFORMS; MAXIMUM LIKELIHOOD ESTIMATION; NEURAL NETWORKS; OPTIMIZATION; RADIAL BASIS FUNCTION NETWORKS; REMOTE SENSING; SECURITY OF DATA; SUSTAINABLE DEVELOPMENT; VEGETATION;

GENETIC ALGORITHMS;

EID: 77958022349     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.860153     Document Type: Conference Paper

References (27)

1. Zhang, L. P., and Chen, L. F., [Hyperspectral remote sensing], Wu Han University Press, Wu Han, 1-2 (2005). (in Chinese).
2. Plaza, A., Benediktsson, J. A., Boardman, J. W., et al., "Recent advances in techniques for hyperspectral image processing," Remote Sensing of Environment. Papers 113, S110-122 (2009).
3. Wang, F., Zhuo, L., Li, X., and Xia, L. H., "Urban Vegetation Stress Level Monitoring Based on Hyperspectral Feature Selection and RBF Neural Network," Scientia Geographica Sinica. Papers 28(1), 71-82 (2008). (in Chinese).
4. Li, S. H., Wang, J. L., Bi, Y., et al., "A review of methods for classification of remote sensing images," Remote Sensing for Land and Resources. Papers 64(2), 1-6 (2005).(in Chinese).
5. King, R. L., "Putting information into the service of decision making: the role of remote sensing analysis," IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 25-29 (2003).
6. Keshava, N. and Mustard, J. F., "Spectral unmixing, "IEEE Signal Processing Magazine, 19, 44-57 (2002).
7. Richards, J. A., "Analysis of remotely sensed data: The formative decades and the future," IEEE Transactions on Geoscience and Remote Sensing. Papers 43, 422-432 (2005).
8. Gualtieri, J. A. and Cromp, R. F., "Support vector machines for hyperspectral remote sensing classification," Proceedings of SPIE, 3584, 221-232 (1998).
9. Gualtieri, J. A., Chettri, S. R., Cromp, R. F., et al., "Support vector machines applied to AVIRIS data," Summaries of the Airborne Earth Science Workshop (1999).
10. Watanachaturaporn, P., Arora, M. K. and Varshney, P. K., "Hyperspectral image classification using support vector machines: A comparison with decision tree and neural network classifiers," American Society for Photogrammetry & Remote Sensing 2005 Annual Conference," Reno, N.V (2006).
11. Goldberg, D., [Genetic algorithms in search, optimization and machine learning], Addison-Wesley, Massachusetts (1989).
12. Cortes, C., Vapnik, V., "Support vector networks," Machine Learning, 20, 273-297 (1995).
13. Courant and Hilbert, [Methods of Mathematical Physics], John Wiley, New York (1953).
14. Huang, C., Davis, L. S., Townshend, J. R. G., "An assessment of support vector machines for land cover classification," International Journal of Remote Sensing. Papers 23(4), 725-749 (2002).
15. Scholkopf, B. and Smola, J., [Learning with kernels], Massachusetts: Massachusetts Institute of Technology Press (2002).
16. Bottou, L., Cortes, C., Vapnik, V., et al., "Comparison of classifier methods: A case study in handwriting digit recognition," Pattern Recognition. Papers 77-87 (1994).
17. Hsu, C. W. and Lin, C. J., "A comparison of methods for multi-class support vector machines," IEEE Trans-actions on Neural Networks, 13, 415-425 (2002).
18. Zheng, S. B., Tang, H. J., Han, Z. Z., et al., "Solving large-scale multiclass learning problems via an efficient support vector classifier," Journal of Systems Engineering and Electronics. Papers 17, 910-915 (2006).
19. Wang, Q., Shu, J., Yin, Q., "DSGF method on detecting and removing spectral noise of hyper spectral image," Journal of Infrared and Millimeter Waves. Papers 25 (1), 29-32 (2006).
20. Hughes, G. F., "On the mean accuracy of statistical pattern recognition," IEEE Transactions on Information Theory, 14, 55-63 (1968).
21. Tong, Q. X., Zhang, B., Zhen, L. F., [Hyperspectral remote sensing: principle, technique and application], Bei Jing: Higher Education Press (2006). (in Chinese).
22. Ayat, N. E., Cheriet, M., Suen, C. Y., "Automatic model selection for the optimization of SVM kernels," Pattern Recognition. Papers 38, 1733-1745 (2005).
23. Wu, Tzeng, Goo, et al., "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert Systems with Applications. Papers 32, 397-408 (2007).
24. Baluja, S., Caruana, R., "Removing the Genetics from the standard genetic algorithm," Proceedings of the Twelfth International Conference on Machine Learing," Lake Tahoe, CA (1995).
25. Min, J. H., Lee, Y. C., "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters," Expert Systems with Applications. Papers 28(4), 603-614 (2005).
26. Marco, R. and Paola, S., "Robust bayes classifiers," Artificial Intelligence. Papers 125, 209-226 (2001).
27. Rivas, V. M., Merelo, J. J., Castillo, P. A., et al., "Evolving RBF neural networks for time-series forecasting with EvRBF," Information Sciences. Papers 165(19), 207-220 (2004).