中文

Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (6): 598-604.doi: 10.3969/j.issn.0255-8297.2011.06.008

• Signal and Information Processing • Previous Articles     Next Articles

Hyperspectral Remote Sensing Image Classification with Extremely Randomized Clustering Forests

XU Hai-wei1, YANG Min-hua1, HAN Rui-mei2, WANG Zhen-xing3;4   

  1. 1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China
    2. School of Surveying & Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000,
    Henan Province, China
    3. School of Metallurgical Science and Engineering, Central South University, Changsha 410083, China
    4. South China Institute of Environmental Sciences, Ministry of Environmental Protection,
    Guangzhou 510655, China
  • Received:2011-01-11 Revised:2011-05-04 Online:2011-11-30 Published:2011-11-30

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Abstract:

Hyperspectral images contain rich spectral information and have better performance in ground target recognition than panchromatic and multispectral images. An extremely randomized clustering forests (ERC-Forests) algorithm is introduced after analysis of the decision tree algorithm. Hyperion hyperspectral images and IRS-p6 image data of Qilian County, Qinghai Province, are used in the experiment. After dimension reduction with subspace methods and based on the spectral range, support vector machine (SVM), neural network (NN) and maximum likelihood (MLC) are used for classification. The results are compared with that of random decision trees algorithm, showing that the extremely randomized clustering forests algorithm is better, without dimension reduction. The method is widely applicable to hyperspectral remote sensing.

Key words: hyperspectral remote sensing, automatic image classification, pattern classification, land cover classification, extremely randomized clustering forests (ERC-forests)

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