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Journal of Spectroscopy
Volume 2015, Article ID 969185, 8 pages
http://dx.doi.org/10.1155/2015/969185
Research Article

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
2Sansom Institute for Health Research and School of Pharmacy and Medical Science, University of South Australia, Adelaide, SA 5001, Australia

Received 25 August 2014; Accepted 13 September 2014

Academic Editor: Tifeng Jiao

Copyright © 2015 Chunyang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. F. H. Goetz, “Three decades of hyperspectral remote sensing of the Earth: a personal view,” Remote Sensing of Environment, vol. 113, supplement 1, pp. S5–S16, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Wufa, Y. Guopeng, and C. Wei, Hyperspectral Image Analysis and Application, Science Press, Beijing, China, 2013.
  3. P. K. Mallupattu and J. R. S. Reddy, “Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India,” The Scientific World Journal, vol. 2013, Article ID 268623, 6 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in hyperspectral image classification: earth monitoring with statistical learning methods,” IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 45–54, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. B. B. Damodaran and R. R. Nidamanuri, “Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system,” Advances in Space Research, vol. 53, no. 12, pp. 1720–1734, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. Wang and L. Liu, “Assessment of coarse-resolution land cover products using CASI hyperspectral data in an arid zone in Northwestern China,” Remote Sensing, vol. 6, no. 4, pp. 2864–2883, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zhang, Y. Du, F. Ling, S. Fang, and X. Li, “Example-based super-resolution land cover mapping using support vector regression,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1271–1283, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Padma and S. Sanjeevi, “Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis,” International Journal of Applied Earth Observation and Geoinformation, vol. 32, pp. 138–151, 2014. View at Publisher · View at Google Scholar
  9. B. T. Zabe, O. O. Olugbara, and T. Marwala, “Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification,” Journal of Earth System Science, vol. 123, no. 4, pp. 779–790, 2014. View at Publisher · View at Google Scholar
  10. M. Pal, “Extreme-learning-machine-based land cover classification,” International Journal of Remote Sensing, vol. 30, no. 14, pp. 3835–3841, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Bao, M. Chi, and J. A. Benediktsson, “Spectral derivative features for classification of hyperspectral remote sensing images: experimental evaluation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 594–601, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Stankevich, V. Levashenko, and E. Zaitseva, “Fuzzy decision tree model adaptation to multi- and hyperspectral imagery supervised classification,” in Proceedings of the 9th International Conference on Digital Technologies (DT '13), pp. 198–202, Žilina, Slovakia, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Alajlan, Y. Bazi, F. Melgani, and R. R. Yager, “Fusion of supervised and unsupervised learning for improved classification of hyperspectral images,” Information Sciences, vol. 217, pp. 39–55, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Ashwini, S. Siddhesh, M. Rakesh, and P. Bhavesh, “A novel approach for object detection in VHR images,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, no. 4, pp. 355–366, 2013. View at Google Scholar
  15. S. Rajan, J. Ghosh, and M. M. Crawford, “An active learning approach to hyperspectral data classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1231–1242, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Jun and J. Ghosh, “Spatially adaptive semi-supervised learning with Gaussian processes for hyperspectral data analysis,” Statistical Analysis and Data Mining, vol. 4, no. 4, pp. 358–371, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. J. Munoz-Mari, D. Tuia, and G. Camps-Valls, “Semisupervised classification of remote sensing images with active queries,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3751–3763, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Jun and J. Ghosh, “Semisupervised learning of hyperspectral data with unknown land-cover classes,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 273–282, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. M. Liu, B. R. Hu, W. J. Wu, and Y. Zhang, “Spectral imaging of green coating camouflage under hyperspectral detection,” Guangzi Xuebao/Acta Photonica Sinica, vol. 38, no. 4, pp. 885–890, 2009. View at Google Scholar · View at Scopus
  20. A. Rényi, “On measures of entropy and information,” in Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 547–561, University of California Press, Berkeley, Calif, USA, 1961.
  21. B. Krishnapuram, L. Carin, M. A. T. Figueiredo, and A. J. Hartemink, “Sparse multinomial logistic regression: fast algorithms and generalization bounds,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 957–968, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Bioucas-Dias and M. Figueeredo, “Logistic regression via variable splitting and augmented lagrangian tools,” Tech. Rep., Instituto Superior Técnico, Lisboa, Portugal, 2009. View at Google Scholar
  23. Y. B. Zhang, J. W. Zhao, J. M. Li, and Y. Sun, “Decision feedback blind equalization algorithm based on RENYI entropy for underwater acoustic channels,” Journal of Electronics and Information Technology, vol. 31, no. 4, pp. 911–915, 2009. View at Google Scholar · View at Scopus
  24. D. Dou, L. Li, and Y. Zhao, “Fault diagnosis of rolling bearings using EEMD- Renyi entropy and PCA-PNN,” Journal of Southeast University (Natural Science Edition), vol. 41, supplement 1, pp. 107–111, 2011. View at Google Scholar
  25. L. T. Jolliffe, Principal Component Analysis, Springer, New York, NY, USA, 2nd edition, 2002. View at MathSciNet