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All issues Volume 68 (2016) MATEC Web Conf., 68 (2016) 18007 References
Open Access
Issue
MATEC Web Conf.
Volume 68, 2016
2016 The 3rd International Conference on Industrial Engineering and Applications (ICIEA 2016)
Article Number 18007
Number of page(s) 6
Section Computer Science and Application
DOI https://doi.org/10.1051/matecconf/20166818007
Published online 01 August 2016
  1. Luo Jiancheng, Zhou Chenghu, Yang Yan. Land-cover and land-use classification based on remote sensing intelligent Geo-interpreting model[J]. Journal of Natural Resources, 2001, 16(2):179–183.(in Chinese) [Google Scholar]
  2. Li Xiubin. A review of the international researches on land use/land cover change [J]. ACTA Geographica Sinica, 1996, 51(67):553–557.(in Chinese) [Google Scholar]
  3. Mai Kaile, Zhang Wenhui. Object-oriented classification approach for remote sensing imagery information extraction in loess hilly-gully region[J]. Transactions of the Chinese Society for Agricultural Machinery2011, 42(4):153–158. (in Chinese) [Google Scholar]
  4. Zhao Chunhui, Qiao Lei. Classification of hyperspectral remote sensing image using improved LS-SVM[J]. Applied Science and Technology, 2008, 35(1):44–52.(in Chinese) [Google Scholar]
  5. Fu Wenjie, Hong Jinyi, Lin Mingsen. A method of land use classification from remote sensing image based on support vector machines and spectral similarity scale[J]. Remote Sensing Technology and application, 2006, 21(1):25–30.(in Chinese) [Google Scholar]
  6. Du Peijun, Liu Sicong, Zheng Hui. Land cover change detection over mining areas based on support vector machine [J]. 2012, 41(2):262–267. (in Chinese) [Google Scholar]
  7. Sun Danfeng,Yang Yihong, Liu Shunxi. Application of high-spatial IKONS remote sensing images in land use classification and change monitoring[J]. Transactions of the CSAE, 2002, 18(2):160–164. (in Chinese) [Google Scholar]
  8. Chen Qihao, Liu Zhimin, Xiuguo Liu, et al. Element-oriented land-use classification of mining area by high spatial resolution remote sensing image[J]. Earth Science—Journal of China University of Geosciences, 2010, 35(3):453–458. (in Chinese) [CrossRef] [Google Scholar]
  9. Zhou Pei, Zhou Shenli. Effect of land use on ecological benefit of farm belt in suburbs[J]. Journal of Ecology and Rural Environment, 2007, 23(4): 6–10. (in Chinese) [Google Scholar]
  10. Zhou Yi, Wu Juan, Qi Li, et al. Test and analysis for detecting land use change by using CBERS-02C satellite image[J]. Mineral Exploration.2012,3(5): 688–694. (in Chinese) [Google Scholar]
  11. Ma Ligang, Zhang Leping, Jinsong Zheng, et al. Land use classification using ZY1-”02C”remote sensing images [J]. Journal of Zhejiang University: Engineering Science, 2013, 47(8):1508–1516. (in Chinese) [Google Scholar]
  12. Wang Haihun, Peng Jiaxiong, Wei Wu, et al. A study of evaluation methods on performance of the multi-source remote sensing image fusion[J]. Computer Engineering and Applications,2003,(25):33–37. (in Chinese) [Google Scholar]
  13. hang Xuegong. Introduction to statistical learning theory and support vector machines[J]. Acta Automatica Sinica, 2000, 26(1):32–42. (in Chinese) [Google Scholar]
  14. V N. Vapnik The Nature of Statistical Learning Theory[M]. Berlin: Springer-Verlag Berlin Heidelberg,1998. [Google Scholar]
  15. C. Hsu, C. Lin A comparison of methods for multiclass support vector machines[J]. IEEE Transactions on Neural Networks, 2002,13 (2):415–425. [Google Scholar]
  16. Zhang Quanming, Liu Huijin. Application of LS-SVM in classification of power quality disturbances[J]. Proceedings of the CSEE,2008,28(1):106–110. (in Chinese) [Google Scholar]
  17. Yang Jiajia, Jiang Qigang, Yongliang Chen, et al. Lithology division for large-scale region segmentation based on LS-SVM and high resolution remote sensing images[J]. Journal of China University of Petroleum(Edition of Natural Science), 2012,36(1):60–67. (in Chinese) [Google Scholar]
  18. Wang Kai, Hou Zhurong, Wang Congli. Intrusion detection based on cross-validation SVM[J]. Journal of Test and Measurement Technology, 2010, 24(5):419–423. (in Chinese) [Google Scholar]
  19. Zhang Jinshui, He Chunyang, Yao Pan, et al. The high spatial resolution RS image classification based on SVM method with the multi-source data, 2006,10(1):49–57. (in Chinese) [Google Scholar]
  20. M. Fauvel, J Chanussot, J A. Benediktsson Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas[J]. EURASIP Journal on Advances in Signal Processing, 2009:1–14 [CrossRef] [Google Scholar]
  21. Guo Hui, Wang Ling, Liu Heping. Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems[J]. Journal of University of Science and Technology Beijing, 2006, 28(3):303–306. (in Chinese) [Google Scholar]

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