• Opto-Electronic Engineering
  • Vol. 43, Issue 11, 62 (2016)
LI Tie1 and ZHANG Xinjun2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3969/j.issn.1003-501x.2016.11.010 Cite this Article
    LI Tie, ZHANG Xinjun. Research of Hyperspectral Remote Sensing Image Classification Based on Extreme Learning Machine[J]. Opto-Electronic Engineering, 2016, 43(11): 62 Copy Citation Text show less
    References

    [1] LI Jiming,JIA Sen,PENG Yanbin. Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm [J]. Opto-Electronic Engineering,2012,39(11):88-94.

    [2] KANG Xudong,LI Shutao,FANG Leyuan,et al. Intrinsic image decomposition for feature extraction of hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892),2015,53(4):2241–2253.

    [3] Ghamisi P,Benediktsson J A. Feature selection based on hybridization of genetic algorithm and particle swarm optimization [J]. IEEE Geoscience and Remote Sensing Letters(S1545-598X),2015,12(2):309–313.

    [4] ZHOU Yicong,PENG Jiangtao,CHEN C L. Philip. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892),2015,53(2):1082–1095.

    [5] KANG Xudong,LI Shutao,FANG Leyuan,et al. Extended random walker-based classification of hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892),2015,53(1):144–153.

    [6] Romero A,Radeva P,Gatta C. Meta-parameter free unsupervised sparse feature learning [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828),2015,37(8):1716–1722.

    [7] Bazi Yakoub,Naif Alajlan,Farid Melgani. Differential evolution extreme learning machine for the classification of hyperspectral images [J]. IEEE Geoscience and Remote Sensing Letters(S1545-598X),2014,11(6):1066–1070.

    [8] ZHANG Lefei,ZHANG Qian,ZHANG Liangpei,et al. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding [J]. Pattern Recognition(S0031-3203),2015,48(10):3102–3112.

    [9] CHEN Chen,LI Wei,SU Hongjun,et al. Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine [J]. Remote Sensing(S2072-4292),2014,6(6):5795–5814.

    [10] CHEN Yushi,ZHAO Xing,JIA Xiuping. Spectral-spatial classification of hyperspectral data based on deep belief network [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(S1939-1404),2015,8(6): 2381–2392.

    [11] YUE Jun,ZHAO Wenzhi,MAO Shanjun,et al. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks [J]. Remote Sensing Letters(S2150-704X),2015,6(6):468–477.

    [12] Soltani-Farani A,Rabiee H R,Hosseini S A. Spatial-aware dictionary learning for hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892),2015,53(1):527–541.

    LI Tie, ZHANG Xinjun. Research of Hyperspectral Remote Sensing Image Classification Based on Extreme Learning Machine[J]. Opto-Electronic Engineering, 2016, 43(11): 62
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