›› 2012, Vol. ›› Issue (06): 1597-1601.

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Classification algorithm of hyperspectral images based on kernel entropy analysis

WANG Ying, GUO Lei, LIANG Nan   

  1. Institute of Automatic, Northwest Polytechnical University, Xi'an 710129, China
  • Received:2011-09-08 Online:2012-11-01

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Abstract: To take advantage of the characteristics of KECA for hyperspectral remote sensing image classification, an approach of sample set selection and C-means classification is proposed. The sample selection is based on convex geometry concepts and C-means classification uses spectral angles as distance metrics in feature space. Experiment results of HYDICE hyperspectral data confirm that the proposed approach can improve classification accuracy effectively.

Key words: information processing, hyperspectral image, Renyi entropy, image classification, kernel entropy component analysis

CLC Number: 

  • TN911.73
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