Superpixel-based sparse representation classifier for hyperspectral image

Min Han, Chengkun Zhang, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper proposes a novel superpixel-based method for the classification of hyperspectral image. A superpixel segmentation algorithm called entropy rate superpixel is applied to extract the spatial contextual information in the hyperspectral image, which can change the size and shape of the superpixel adaptively according to spatial structures. Then, a joint sparse representation model is applied to approximate the pixels within each superpixel using a certain number of common samples from a given dictionary in the form of sparse linear combination. Here we use a greedy algorithm called simultaneous orthogonal matching pursuit to pursue the optimal sparse coefficients matrix and a new kind of classification criterion is tested and used to determine the classification results. Experimental results on the Indian Pines hyperspsectral image demonstrate that the proposed method can explore the spatial information effectively and give promising performance when compared with several state-of-art classification methods.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3614-3619
Number of pages6
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 31 Oct 2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • Entropy rate superpixel segmentation
  • Hyperspectral image
  • Joint sparse representation
  • Spectral-spatial classification

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