Joint bilateral filtering and spectral similarity-based sparse representation: a generic framework for effective feature extraction and data classification in hyperspectral imaging

Tong Qiao, Zhijing Yang, Jinchang Ren, Peter Yuen, Huimin Zhao, Genyun Sun, Stephen Marshall, Jon Atli Benediktsson

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22 Citations (Scopus)
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Classification of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for training. It is found that hyperspectral image classification can be generally improved only if the feature extraction technique and the classifier are both addressed. In this paper, a novel classification framework for hyperspectral images based on the joint bilateral filter and sparse representation classification (SRC) is proposed. By employing the first principal component as the guidance image for the joint bilateral filter, spatial features can be extracted with minimum edge blurring thus improving the quality of the band-to-band images. For this reason, the performance of the joint bilateral filter has shown better than that of the conventional bilateral filter in this work. In addition, the spectral similarity-based joint SRC (SS-JSRC) is proposed to overcome the weakness of the traditional JSRC method. By combining the joint bilateral filtering and SS-JSRC together, the superiority of the proposed classification framework is demonstrated with respect to several state-of-the-art spectral-spatial classification approaches commonly employed in the HSI community, with better classification accuracy and Kappa coefficient achieved.
Original languageEnglish
Pages (from-to)316-328
Number of pages13
JournalPattern Recognition
Early online date10 Oct 2017
Publication statusPublished - 31 May 2018



  • hyperspectral imaging
  • joint bilateral filtering
  • sparse representation
  • feature extraction
  • data classification

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