Optics and Precision Engineering, Volume. 20, Issue 6, 1398(2012)

Hyperspectral image classification by steepest ascent relevance vector machine

DONG Chao* and TIAN Lian-fang
Author Affiliations
  • [in Chinese]
  • show less
    References(14)

    [1] [1] HUGHES G F. On the mean accuracy of statistical pattern recognizers [J]. IEEE Transactions on Information Theory, 1968,14(1):55-63.

    [2] [2] TADJUDIN S, LANDGREBE D A. Covariance estimation with limited training samples [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37(4):2113-2118.

    [3] [3] KUO B C, LANDGREBE D A. Nonparametric weighted feature extraction for classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(5):1096-1105.

    [4] [4] SERPICO S B, BRUZZONE L. A new search algorithm for feature selection in hyperspectral remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(7):1360-1367.

    [5] [5] DUNDAR M M, LANDGREBE D A. A cost-effective semisupervised classifier approach with kernels [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(1):264-270.

    [6] [6] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(8):1778-1790.

    [7] [7] BAZI Y, MELGANI F. Toward an optimal SVM classification system for hyperspectral remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(11):3374-3385.

    [8] [8] TIPPING M E. Sparse bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001, 1:211-244.

    [9] [9] DEMIR B, ERTURK S. Hyperspectral image classification using relevance vector machines [J]. IEEE GeoScience and Remote Sensing Letters, 2007,4(4):586-590.

    [10] [10] FOODY G M. RVM-based multi-class classification of remotely sensed data [J]. International Journal of Remote Sensing, 2008,29(6):1817-1823.

    [11] [11] CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al.. Retrieval of oceanic chlorophyll concentration with relevance vector machines [J]. Remote Sensing of Environment, 2006,105:23-33.

    [12] [12] DONG CH, ZHAO H J. Hyperspectral image classification and application based on relevance vector machine [J]. Journal of Remote Sensing, 2010,14(6):1279-1284.(in Chinese)

    [13] [13] DONG CH, TIAN L F, ZHAO H J. Hyperspectral image classification by genetic relevance vector machine [J]. Journal of Shanghai Jiao Tong University, 2011,45(10):1516-1520.(in Chinese)

    [14] [14] AVIRIS N W. Indiana’s Indian Pines Data Set [DB/OL].ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C, 1992.

    CLP Journals

    [1] SUN Qian, FENG Hao, ZENG Zhou-mo. Recognition of optical fiber pre-warning system based on image processing[J]. Optics and Precision Engineering, 2015, 23(2): 334

    [2] Wang Xiaofei, Wang Xiaoyi, Shi Xiangyu, Yan Qiujing, Chen Xiangnan. Target detection algorithm based on spatial-contextual image one class classification[J]. Infrared and Laser Engineering, 2015, 44(S): 236

    [3] LIU Jia-min, LUO Fu-lin, HUANG Hong, LIU Yi-zhe. Classification of Hyperspectral remote sensing images using correlation neighbor LLE[J]. Optics and Precision Engineering, 2014, 22(6): 1668

    [4] WANG Ling, LIU De-ying, JI Chang-ying. Comparison of two feature selection algorithms oriented to raw cotton ripeness discrimination[J]. Optics and Precision Engineering, 2013, 21(8): 2121

    Tools

    Get Citation

    Copy Citation Text

    DONG Chao, TIAN Lian-fang. Hyperspectral image classification by steepest ascent relevance vector machine[J]. Optics and Precision Engineering, 2012, 20(6): 1398

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Feb. 9, 2012

    Accepted: --

    Published Online: Jun. 25, 2012

    The Author Email: Chao DONG (dcAuto@scut.edu.cn)

    DOI:10.3788/ope.20122006.1398

    Topics