Hybrid Bayesian Classifier for Improved Classification Accuracy

HYBRID BAYESIAN CLASSIFIER FOR IMPROVED CLASSIFICATION ACCURACY

Uttam Kumar [uttam@ces.iisc.ernet.in]	S. Kumar Raja [s.kumar.raja@yahoo.com]	C. Mukhopadhyay [cm@mgmt.iisc.ernet.in]	T. V. Ramachandra^* [cestvr@ces.iisc.ernet.in]
Citation: Uttam Kumar, S. Kumar Raja, C. Mukhopadhyay and T. V. Ramachandra , 2011, Hybrid Bayesian Classifier for Improved Classification Accuracy. IEEE Geoscience and Remote Sensing letters, Vol. 8, No. 3, pp. 473 – 476.

CONCLUSION

The major contribution of this technique lies in the fact that abundance estimates from LS-HSR data were utilized as prior probabilities to classify HS-LSR data using a Bayesian classifier improving the overall accuracy by 6% and 9% with IRS LISS-III MS and IKONOS MS data respectively, as compared to conventional Bayesian classifier, demonstrating the robustness of the approach.

^*T. V. Ramachandra, Senior Member, IEEE, is with the Centre for Ecological Sciences, Centre for Sustainable Technologies and Centre for Infrastructure, Sustainable Transport and Urban Planning, Indian Institute of Science (IISc), Bangalore, India.
(Corresponding author phone: 91-80-22933099; fax: 91-80-23601428; e-mail: cestvr@ces.iisc.ernet.in).

Uttam Kumar, Student Member, IEEE, is with the Department of Management Studies and Centre for Sustainable Technologies, Indian Institute of Science, India. (e-mail: uttam@ces.iisc.ernet.in).

Chiranjit Mukhopadhyay is with the Department of Management Studies, Indian Institute of Science, Bangalore, India (e-mail: cm@mgmt.iisc.ernet.in).

S. Kumar Raja is with the VISTA Group, IRISA, Rennes, France and Thomson R&D France, SNC Cesson - Sévigné, France (email: s.kumar.raja@yahoo.com).