576
Views
31
CrossRef citations to date
0
Altmetric
Research Articles

A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data

, &
Pages 196-209 | Received 04 Feb 2014, Accepted 21 Apr 2014, Published online: 04 Jun 2014

References

  • Benediktsson, J.A. and Kanellopoulos, I., 1999. Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1367–1377. doi:10.1109/36.763301.
  • Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2 (2), 121–167. doi:10.1023/A:1009715923555.
  • Chica-Olmo, M. and Abarca-Hernández, F., 2004. Variogram derived image texture for classifying remotely sensed images. In: S.M. de jong and F.D. van der Meer, eds. Remote sensing image analysis: including the spatial domain. 93–111.
  • Dalponte, M., Bruzzone, L., and Gianelle, D., 2008. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1416–1427. doi:10.1109/TGRS.2008.916480.
  • Dong, J., et al., 2009. Advances in multi-sensor data fusion: algorithms and applications. Journal of Sensors, 9 (10), 7771–7784. doi:10.3390/s91007771.
  • Du, P., et al., 2013. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14 (1), 19–27. doi:10.1016/j.inffus.2012.05.003.
  • Haralick, R., Shanmugam, K., and Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3 (6), 610–621. doi:10.1109/TSMC.1973.4309314.
  • Hsu, C.-W., Chung, C.-C., and Lin, C.-J., 2010. A practical guide to support vector classification [online]. Taipei, National Taiwan University. Available from: http://www.csie.ntu.edu.tw/_cjlin [Accessed 13 March 2010].
  • Hsu, S. and Burke, H., 2003. Multisensor fusion with hyperspectral imaging data detection and classification. Lincoln Laboratory Journal, 14 (1), 145–159.
  • Huang, Y.S. and Suen, C.Y., 1993. Behavior-knowledge space method for combination of multiple classifiers. In: Proceedings of IEEE computer vision and pattern recognition. New York, NY: IEEE, 347–352.
  • Huang, Y.S. and Suen, C.Y., 1995. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (1), 90–94. doi:10.1109/34.368145.
  • Kuncheva, L., 2004. Combining pattern classifiers methods and algorithms. Hoboken, NJ: John Wiley & Sons.
  • Kuncheva, L.I., Bezdek, J.C., and Duin, R., 2001. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition, 34 (2), 299–314. doi:10.1016/S0031-3203(99)00223-X.
  • Lam, L. and Suen, S.Y., 1997. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 27 (5), 553–568. doi:10.1109/3468.618255.
  • Lin, X., et al., 2003. Performance analysis of pattern classifier combination by plurality voting. Pattern Recognition Letters, 24, 1959–1969. doi:10.1016/S0167-8655(03)00035-7.
  • Littlestone, N. and Warmuth, M., 1994. The weighted majority algorithm. Information and Computation, 108, 212–261. doi:10.1006/inco.1994.1009.
  • McArthur, S.D.J., Strachan, S.M., and Jahn, G., 2004. The design of a multi-agent transformer condition monitoring system. IEEE Transactions on Power Systems, 19 (4), 1845–1852. doi:10.1109/TPWRS.2004.835667.
  • Parikh, C.R., Pont, M.J., and Jones, N.B., 2001. Application of Dempster–Shafer theory in condition monitoring applications: a case study. Pattern Recognition Letters, 22, 777–785. doi:10.1016/S0167-8655(01)00014-9.
  • Parikh, C.R., et al., 2003. Improving the performance of CMFD applications using multiple classifiers and a fusion framework. Transactions of the Institute of Measurement and Control, 25 (2), 123–144. doi:10.1191/0142331203tm080oa.
  • Pohl, C. and Van Genderen, J., 1998. Review article multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19 (5), 823–854. doi:10.1080/014311698215748.
  • Rahman, A.F.R., Alam, H., and Fairhurst, M.C., 2002. Multiple classifier combination for character recognition: revisiting the majority voting system and its variations. In: IAPR workshop on document analysis systems, Vol. 2423, August 2002 Princeton, NJ. Berlin: Springer, 167–178.
  • Rahman, A.F.R. and Fairhurst, M. 2000. Decision combination of multiple classifiers for pattern classification: hybridisation of majority voting and divide and conquer techniques. In: 5th IEEE workshop on applications of computer vision, WACV 2000, 4–6 December 2000. Palm Springs, CA, 58–63.
  • Ruta, D. and Gabrys, B., 2000. An overview of classifier fusion methods. Computation Information. System, 7 (1), 1–10.
  • Schölkopf, B. and Smola, A., 2002. Learning with kernels. Cambridge, MA: MIT Press.
  • Simone, G., et al., 2002. Image fusion techniques for remote sensing applications. Information Fusion, 3 (1), 3–15. doi:10.1016/S1566-2535(01)00056-2.
  • Swatantran, A., et al., 2011. Mapping biomass and stress in the Sierra Nevada using LIDAR and hyperspectral data fusion. Remote Sensing of Environment, 115 (11), 2917–2930. doi:10.1016/j.rse.2010.08.027.
  • Tsoumakas, G., Angelis, L., and Vlahavas, I., 2005. Selective fusion of heterogeneous classifiers. Intelligent Data Analysis, 9 (6), 511–525.
  • Uhlmann, S., Kiranyaz, S., and Yildirm, A., 2013. Evaluation of classifiers for polarimetric SAR classification. In: IEEE international geosciences and remote sensing symposium, 21–26 July 2013 Melbourne, Australia.
  • Vapnik, V.N., 1998. Statistical learning theory. New York, NY: Wiley.
  • Xu, L., Krzyzak, A., and Suen, C.Y., 1992. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 22 (3), 418–435. doi:10.1109/21.155943.
  • Yun, Z., 2004. Understanding image fusion. Photogrammetry Engineering and Remote Sensing, 6 (1), 657–661.
  • Zhao, B., et al., 2013. Hybrid generative/discriminative scene classification strategy based on latent Dirichlet allocation for high spatial resolution remote sensing imagery. In: IEEE international geosciences and remote sensing symposium, 21–26 July 2013 Melbourne, Australia.
  • Zheng, M.M., Krishnan, S.M., and Tjoa, M.P., 2005. A fusion-based clinical decision support for disease diagnosis from endoscopic images. Computers in Biology and Medicine, 35, 259–274. doi:10.1016/j.compbiomed.2004.01.002.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.