http://www.iisc.ernet.in/
Random Forest Algorithm with derived Geographical Layers for Improved Classification of Remote Sensing Data
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. V. Ramachandra and U. Kumar, “Land Surface Temperature with Land Cover Dynamics: Multi-Resolution, Spatio-Temporal Data Analysis of Greater Bangalore,” Int. J. Geoinformatics, vol. 5, no. 3, pp. 43-53, 2009.
  2. D. Lu and Q. Weng, “Urban Classification Using Full Spectral Information of Landsat ETM+ Imagery in Marion County, Indiana,” Photogramm. Eng. Remote Sens., vol. 71, no. 11, pp. 1275-1284, 2005.
  3. D. Lu and Q. Weng, “A survey of image classification methods and techniques for improving classification performances,” Int. J. Remote Sensing, vol. 28, no. 5, pp. 823-870, 2007.
  4. J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall, Upper Saddle River, New Jersey, 2000.
  5. D. Lu, S. Hetrick and E. Moran, “Land Cover Classification in a Complex Urban-Rural landscape with Quickbird Imagery,” Photogramm. Eng. Remote Sens., vol. 76, no. 10, pp. 1159-1168, 2010.
  6. T. Rashed, “Remote sensing of within class change in urban neighbourhood structures,” Computers, Environ. & Urban Sys., vol. 32, pp. 343-354, 2008.
  7. B. N. Haack, E. K. Solomon, M. A. Bechdol and N. D. Herold, “Radar and optical data comparison/integration for urban delineation: a case study,” Photogramm. Eng. Remote Sens., vol. 68, pp.1289-1296, 2002.
  8. J. Stuckens, P. R. Coppin and M. E. Bauer, “Integrating contextual information with per-pixel classification for improved land cover classification,” Remote Sens. Environ., vol. 71, pp. 282-296, 2000.
  9. X. Na, S. Zhang, X. Li, H. Yu and C. Liu, “Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic mapper Imagery and Ancillary Geographic Data,” Photogramm. Eng. Remote Sens., vol. 76, no. 7, pp. 833-840, 2010.
  10. Na. Xiaodong, Z. Shuqing, Z. Huaiqing, L. Xiaofeng, L. Chunyue, “Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area,” Chinese Geographical Sc., vol. 19, no. 2, pp. 177-185, 2009.
  11. A. Fahsi, T. Tsegaye, W. Tadesse and T. Coleman, “Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy,” Forest Ecology and Management, vol. 128(2000), pp. 57-64, 2000.
  12. J. A. Recio, L. A. Hermosilla and A. Fernandez-Sarria, “Historical Land Use as a Feature for Image Classification,” Photogramm. Eng. Remote Sens., vol. 77, no. 4, pp. 377-387, 2011.
  13. M. Masocha and A. K. Skidmore, “Integrating conventional classifiers with a GIS expert system to increase the accuracy of invasive species mapping,” Int. J. Applied Earth Observation and Geoinformation, vol. 13, pp. 487-494, 2011.
  14. G. Xian, M. Crane and C. McMahon, “Quantifying Multi-temporal Urban development Characteristics in Las Vegas from Landsat and ASTER Data,” Photogramm. Eng. Remote Sens., vol. 74, no. 4, pp. 473-481, 2008.
  15. L. Breiman, “Random Forests,” Machine Learning, vol. 40, pp. 5-32, 2001.
  16. L. Breiman and A. Cutler, “Random Forests”, 2005. URL: http://www.stat.berkeley.edu/users/breiman/RandomForests/
  17. L. Breiman and A. Cutler, “Breiman and Cutler’s random forests for classification and regression,” Version 4.5-36, Repository – CRAN, 2010.
  18. J. Zhu, J. Shi, H. Chu, J. Hu, X. Li and W. Li, “Remote Sensing Classification Using Fractal Dimension over a Subtropical Hilly Region,” Photogramm. Eng. Remote Sens., vol. 77, no. 1, pp. 65-74, 2011.
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Citation : Uttam Kumar, Anindita Dasgupta, Chiranjit Mukhopadhyay and Ramachandra. T.V., 2011, Random Forest Algorithm with derived Geographical Layers for Improved Classification of Remote Sensing Data., Proceedings of the INDICON 2011, Engineering Sustainable Solutions, 16-18th December, Hyderabad - India, pp. 1-6.
* 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
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