• Journal of Infrared and Millimeter Waves
  • Vol. 33, Issue 3, 311 (2014)
LIU Pei1、*, DU Pei-Jun2, and TAN Kun1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3724/sp.j.1010.2014.00311 Cite this Article
    LIU Pei, DU Pei-Jun, TAN Kun. A novel remotely sensed image classification based on ensemble learning and feature integration[J]. Journal of Infrared and Millimeter Waves, 2014, 33(3): 311 Copy Citation Text show less
    References

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    [4] Zhong Ping, Wang Runsheng. A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2007,45(12): 39783988.

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    [8] Gamba P, DellAcqua F, Dasarathy V. Urban remote sensing using multiple data sets: Past, present, and future[J]. Information Fusion, 2005,6(4): 319326.

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    [16] Du Peijun, Xia Junshi, Zhang Wei, et al.Multiple Classifier System for Remote Sensing Image Classification: A Review[J]. Sensors, 2012,12(4): 47644792.

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    [19] Du Peijun, Chen Yu, Xia Junshi, et al. A novel remote sensing image classification scheme based on data fusion, multiple features and ensemble learning[J]. J Indian Soc Remote sensing, 2013,41(2): 213222

    [20] Zhang Caiyu, Xie Zhixiao. Data fusion and classifier ensemble techniques for vegetation mapping in the coastal everglades. Geocarto International, 2012(35): 16.

    LIU Pei, DU Pei-Jun, TAN Kun. A novel remotely sensed image classification based on ensemble learning and feature integration[J]. Journal of Infrared and Millimeter Waves, 2014, 33(3): 311
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