http://www.iisc.ernet.in/
Advanced Machine Learning Algorithms based Free and Open Source Packages for Landsat ETM+ Data Classification
http://wgbis.ces.iisc.ernet.in/energy/
Uttam Kumar1,2,3          Anindita Dasgupta1          Chiranjit Mukhopadhyay2           T.V. Ramachandra1,3,4,*
1Energy and Wetlands Research Group, Centre for Ecological Sciences [CES], 2Department of Management Studies, 3Centre for Sustainable Technologies (astra),
4Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP], Indian Institute of Science, Bangalore – 560012, India.
*Corresponding author:
cestvr@ces.iisc.ernet.in

REFERENCES

  1. T. Kavzoglu and I. Colkesen, “A kernel functions analysis for support vector machines for land cover classification,” Int. J. Applied Earth Obs Geoinformation, vol. 11, no. 5, pp. 352–359, 2009.
  2. R.J. Roiger and M.W. Geatz, Data Mining: A Tutorial-Based Primer, Addison Wesley, Boston. 2003.
  3. M. Tseng, S. Chen, G. Hwang and M. Shen, “A genetic algorithm rule-based approach for land-cover classification,” ISPRS J. Photogramm Remote Sens, vol. 63, pp. 202–212, 2008.
  4. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2003.
  5. Y.V. Venkatesh and S. KumarRaja, “On the classification of multispectral satellite images using the multilayer perceptron,” Pattern Recognition, vol. 36, pp. 2161–2175, 2003.
  6. S. Piramuthu, “Input Data for Decision Trees. Expert Systems with Applications,”  2006, doi: 10.1016/j.eswa.2006.12.030.
  7. U. Kumar, N. Kerle, M. Punia and T.V. Ramachandra, “Mining Land Cover Information using Multilayer Perceptron and Decision Tree from MODIS data,J. the Indian Society of Remote Sens, vol. 38, no. 4, pp. 592–603, 2011.
  8. B.V. Dasarathy, (Editor), Nearest neighbor (NN) norms: NN pattern classification techniques, IEEE Computer Society Press, Los Alamitos, California, 1990.
  9. P.J. Hardin, “Parametric and Nearest-neighbor methods for hybrid classification: A comparison of pixel assignment accuracy,” Photogramm Eng Remote Sens, vol. 60, pp. 1439–1448, 1994.
  10. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall International, Englewood Cli6s, NJ, 1999.
  11. L. Zhou and X. Yang, “Training Algorithm Performance for Image Classification by Neural Networks,” Photogramm Eng Remote Sens, vol. 76, no. 8, pp. 945–951, 2010.
  12. L. Breiman, “Random Forests,” Machine Learning, vol. 40, pp. 5–32, 2001.
  13. L. Breiman, J. Friedman, C.J. Stone and R.A. Olshen, Classification and Regression Trees, 3rd Ed., CRC Press, Boca Raton, Fl., pp. 372, 1998.
  14. P.O. Gislason, J.A. Benediktsson, J.R. Sveinsson, “Random Forests for land cover classification,” Pattern Recognition Letters, vol. 27, pp. 294–300, 2006.
  15. C. Bouman and M.A. Shapiro, “Multiscale Random Field Model for Bayesian Image Segmentation,” IEEE Trans Image Process, vol. 3, no. 2, pp. 162–177, 1994.
  16. X. Yang, “Parameterizing Support Vector Machines for Land Cover Classification,” Photogramm Eng Remote Sens, vol. 77, no. 1, pp. 27–37, 2011.
  17. S. Magnussen, P. Boudewyn and M. Wulder, “Contextual classification of Landsat TM images to forest inventory cover types,” Int J. Remote Sens, vol. 25, pp. 2421–2440, 2004.
  18. H. Eva, S. Carboni, F. Achard, N. Stach, L. Durieux, and J-F. Faure, “Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery,” ISPRS J. Photogramm Remote Sens, vol. 65, no. 2, pp. 191-197, 2010.
  19. J.L. Dungan, Towards a comprehensive view of uncertainty in remote sensing analysis. In Foody, G.M. and Atkinson, P.M. (Eds), Uncertainty in Remote Sensing and GIS, Chichester: John Wiley & Sons, pp. 23-35, 2002.
BACK   «   TOP   »   NEXT
Citation :Uttam Kumar, Anindita Dasgupta, Chiranjit Mukhopadhyay and Ramachandra. T.V., 2012, Advanced Machine Learning Algorithms based Free and Open Source Packages for Landsat ETM+ Data Classification., Proceedings of the OSGEO-India: FOSS4G 2012- First National Conference "OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH” 25-27th October 2012, @ IIIT Hyderabad , pp. 1-7.
* Corresponding Author :
Dr. T.V. Ramachandra
Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore – 560 012, India.
Tel : +91-80-2293 3099/2293 3503-extn 107,      Fax : 91-80-23601428 / 23600085 / 23600683 [CES-TVR]
E-mail : cestvr@ces.iisc.ernet.in, energy@ces.iisc.ernet.in,     Web : http://wgbis.ces.iisc.ernet.in/energy, http://ces.iisc.ernet.in/grass
E-mail    |    Sahyadri    |    ENVIS    |    GRASS    |    Energy    |      CES      |      CST      |    CiSTUP    |      IISc      |    E-mail