Spectral-spatial hyperspectral image classification with edge-preserving filtering

Xudong Kang, Shutao Li, Jon Atli Benediktsson

Research output: Contribution to journalArticlepeer-review

455 Citations (Scopus)


The integration of spatial context in the classification of hyperspectral images is known to be an effective way in improving classification accuracy. In this paper, a novel spectral-spatial classification framework based on edge-preserving filtering is proposed. The proposed framework consists of the following three steps. First, the hyperspectral image is classified using a pixelwise classifier, e.g., the support vector machine classifier. Then, the resulting classification map is represented as multiple probability maps, and edge-preserving filtering is conducted on each probability map, with the first principal component or the first three principal components of the hyperspectral image serving as the gray or color guidance image. Finally, according to the filtered probability maps, the class of each pixel is selected based on the maximum probability. Experimental results demonstrate that the proposed edge-preserving filtering based classification method can improve the classification accuracy significantly in a very short time. Thus, it can be easily applied in real applications.

Original languageEnglish
Article number6553593
Pages (from-to)2666-2677
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
Publication statusPublished - May 2014

Other keywords

  • Classification
  • edge-preserving filters (EPFs)
  • hyperspectral data
  • spatial context


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