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Mathematical Problems in Engineering
Volume 2015, Article ID 182439, 8 pages
http://dx.doi.org/10.1155/2015/182439
Research Article

Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

1The Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
2The State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety and Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received 8 July 2015; Accepted 29 October 2015

Academic Editor: Fons J. Verbeek

Copyright © 2015 Shi He 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. L. Wang, W. P. Sousa, and P. Gong, “Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery,” International Journal of Remote Sensing, vol. 25, no. 24, pp. 5655–5668, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Salehi, Y. Zhang, M. Zhong, and V. Dey, “Object-based classification of urban areas using VHR imagery and height points ancillary data,” Remote Sensing, vol. 4, no. 8, pp. 2256–2276, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Kim, M. Madden, and T. A. Warner, “Forest type mapping using object-specific texture measures from multispectral Ikonos imagery: segmentation quality and image classification issues,” Photogrammetric Engineering and Remote Sensing, vol. 75, no. 7, pp. 819–829, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Shen, H. Tang, Y. Chen, A. Gong, J. Li, and W. Yi, “A semisupervised latent dirichlet allocation model for object-based classification of VHR panchromatic satellite images,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 4, pp. 863–867, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” The Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993–1022, 2003. View at Google Scholar · View at Scopus
  6. T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '99), pp. 50–57, Berkeley, Calif, USA, August 1999.
  7. G. Cheng, L. Guo, T. Zhao, J. Han, H. Li, and J. Fang, “Automatic landslide detection from remote-sensing imagery using a scene classification method based on boVW and pLSA,” International Journal of Remote Sensing, vol. 34, no. 1, pp. 45–59, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Qi, H. Tang, Y. Shu, L. Shen, J. Yue, and W. Jiang, “An object-oriented clustering algorithm for VHR panchromatic images using nonparametric latent Dirichlet allocation,” in Proceedings of the 32nd IEEE International Geoscience and Remote Sensing Symposium (IGARSS '12), pp. 2328–2331, IEEE, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Li, H. Tang, S. He et al., “Unsupervised detection of earthquake-triggered roof-holes from UAV images using joint color and shape features,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 9, pp. 1823–1827, 2015. View at Publisher · View at Google Scholar
  10. H. Tang, L. Shen, Y. Qi et al., “A multiscale latent dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1680–1692, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Yi, H. Tang, and Y. Chen, “An object-oriented semantic clustering algorithm for high-resolution remote sensing images using the aspect model,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 3, pp. 522–526, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Hu, W. Yang, J. Chen, and H. Sun, “Tile-level annotation of satellite images using multi-level max-margin discriminative random field,” Remote Sensing, vol. 5, no. 5, pp. 2275–2291, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. J. D. Mcauliffe and D. M. Blei, “Supervised topic models,” in Advances in Neural Information Processing Systems, pp. 121–128, MIT Press, 2008. View at Google Scholar
  14. S. Lacoste-Julien, F. Sha, and M. I. Jordan, “DiscLDA: discriminative learning for dimensionality reduction and classification,” in Proceedings of the Advances in Neural Information Processing Systems (NIPS '09), pp. 897–904, June 2009.
  15. J. Zhu, A. Ahmed, and E. P. Xing, “MedLDA: maximum margin supervised topic models for regression and classification,” in Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1257–1264, Montreal, Canada, June 2009.
  16. X. Zhu, “Semi-supervised learning literature survey,” Technical Note, 2008. View at Google Scholar
  17. J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with fast sampling algorithms,” in Proceedings of the 30th International Conference on Machine Learning (ICML '13), pp. 124–132, Atlanta, Ga, USA, June 2013.
  18. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 839–846, Bombay, India, January 1998. View at Publisher · View at Google Scholar · View at Scopus
  19. X. Kang, S. Li, and J. A. Benediktsson, “Spectral-spatial hyperspectral image classification with edge-preserving filtering,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2666–2677, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Zhu and E. P. Xing, “Conditional topic random fields,” in Proceedings of the 27th International Conference on Machine Learning (ICML '10), pp. 1239–1246, June 2010. View at Scopus
  21. G. Heinrich, “Parameter estimation for text analysis,” Technical Note, 2005. View at Google Scholar
  22. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, “Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 8, pp. 2973–2987, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, “Entropy rate superpixel segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 2097–2104, IEEE, Providence, RI, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus