Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels

Leyuan Fang, Shutao Li, Wuhui Duan, Jinchang Ren, Jon Atli Benediktsson

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For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very similar spectral characteristics. Firstly, the proposed SC-MK method adopts an over-segmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information as well as spatial information within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machines classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date2 Jul 2015
Publication statusPublished - 2015



  • hyperspectral image
  • superpixel
  • multiple kernels
  • spectral-spatial image classification
  • support vector machines

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