http://www.iisc.ernet.in/
HYBRID BAYESIAN CLASSIFIER FOR IMPROVED CLASSIFICATION ACCURACY
http://wgbis.ces.iisc.ernet.in/energy/

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.

REFERENCES

  1. J. Ediriwickrema and S. Khorram, “Hierarchical Maximum-Likelihood Classification for Improved Accuracies,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 4, pp. 810-816, 1997.
  2. D. K. McIver and M. A. Friedl, “Using prior probabilities in decision-tree classification of remotely sensed data,” Remote Sens. Environ., vol. 81, pp. 253-261, 2002.
  3. C. Lee and D. A. Landgrebe, “Fast likelihood classification,” IEEE Trans. Geosci. Remote Sens., vol. 29, no. 4, pp. 509–517, 1991.
  4. L. Pedroni, “Improved classification of Landsat Thematic Mapper data using modified prior probabilities in large and complex landscapes,” Int. J. Remote Sens., vol.24, no. 1, pp. 91-113, 2003.
  5. Z. Mingguo, C. Qiangguo and Q. Mingzhou, “The Effect of Prior Probabilities in the Maximum Likelihood Classification on Individual Classes: A Theoretical Reasoning and Empirical Testing,” Photogramm Eng. Remote Sens., vol. 75, no. 9, pp. 1109-1116, 2009.
  6. L. L. F. Janssen and H. Middelkoop, “Knowledge-based crop classification of a Landsat Thematic Mapper image,” Int. J. Remote Sensing, vol. 13, pp. 2827–2837, 1992.
  7. H. Cetin, T. A. Warner and D. W. Levandowski, “Data classification, visualization, and enhancement using n-dimensional probability density functions (NPDF): AVIRIS, TIMS, TM, and geophysical applications,” Photogramm. Eng. Remote Sens., vol. 59, pp. 1755–1764, 1993.
  8. A. H. Strahler, “The use of prior probabilities in maximum likelihood classification of remotely sensed data,” Remote Sens. Environ., vol. 10, pp. 135–163, 1980.
  9. G. Storvik, R. Fjortoft, and A. H. S. Solberg, “A Bayesian Approach to Classification of Multiresolution Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 539–547, 2005.
  10. B. Zhukov, D. Oertel, F. Lanzl, and G. Reinhackel, “Unmixing-based Multisensor Multiresolution Image Fusion” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1212–1226, 1999.
  11. U. Kumar, N. Kerle, and T. V. Ramachandra, “Constrained linear spectral unmixing technique for regional land cover mapping using MODIS data,” In: Innovations and advanced techniques in systems, computing sciences and software engineering/ed by Khaled Elleithy, Berlin: Springer, pp. 87-95, 2008.
  12. C-I. Chang, “Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 502–518, 2005.
  13. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2003.
  14. M. E. Winter, “N-Findr: an algorithm for fast autonomous spectral end-member determination in hyperspectral data,” In Proceedings of the SPIE: Imaging Spectrometry, vol. 3753, pp. 266-275, 1999.
  15. A. Plaza, P. Martinez, R. Perez, and J. Plaza, “A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650–663, 2004.
  16. C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 1083–1094, 2000.
  17. C. Song, “Spectral mixture analysis for sub-pixle vegetation fractions in the urban environment: How to incorporate endmember variability?” Remote Sens. Environment, vol. 95, pp. 248–263, 2005.
  18. G. M. Foody, and H. T. X. Doan, “Variability in Soft Classification Prediction and its Implications for Sub-pixel Scale Change Detection and Super Resolution Mapping” Photogrammetric Engineering & Remote Sensing, vol. 73, no. 8, pp. 923–933, 2007.

*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).

E-mail   |   Sahyadri   |   ENVIS   |   GRASS   |   Energy   |   CES   |   CST   |   CiSTUP   |   IISc   |   E-mail