Extended random walker-based classification of hyperspectral images

Xudong Kang, Shutao Li, Leyuan Fang, Meixiu Li, Joń Atli Benediktsson

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)


This paper introduces a novel spectral - spatial classification method for hyperspectral images based on extended random walkers (ERWs), which consists of two main steps. First, a widely used pixelwise classifier, i.e., the support vector machine (SVM), is adopted to obtain classification probability maps for a hyperspectral image, which reflect the probabilities that each hyperspectral pixel belongs to different classes. Then, the obtained pixelwise probability maps are optimized with the ERW algorithm that encodes the spatial information of the hyperspectral image in a weighted graph. Specifically, the class of a test pixel is determined based on three factors, i.e., the pixelwise statistics information learned by a SVM classifier, the spatial correlation among adjacent pixels modeled by the weights of graph edges, and the connectedness between the training and test samples modeled by random walkers. Since the three factors are all well considered in the ERW-based global optimization framework, the proposed method shows very good classification performances for three widely used real hyperspectral data sets even when the number of training samples is relatively small.

Original languageEnglish
Article number2319373
Pages (from-to)144-153
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
Publication statusPublished - Jan 2015

Other keywords

  • Graph
  • Hyperspectral image
  • Index terms - Extended random walkers (ERWs)
  • Optimization
  • Spectral-spatial image classification.


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