An effective collaborative representation algorithm for hyperspectral image classification

Sen Jia, Lin Deng, Linlin Shen

Research output: Journal PublicationConference articlepeer-review

2 Citations (Scopus)

Abstract

In this paper, an effective l2-norm collaborative representation algorithm based on 3D discrete wavelet transform (3D-DWT) features, called CR-DWT, is proposed for hyperspec-tral image classification. By using the discriminative 3D-DWT features extracted from the original spectral space, a non-parametric and efficient l2-norm CR method is developed to calculate the representation coefficients. Due to the simplicity of the method, the computational cost has been substantially reduced, thus all the extracted 3D-DWT texture features can be directly utilized to code the test sample, which greatly improves the classification accuracy of the l2-norm CR mechanism. The extensive experiments on two real hy-perspectral data sets have shown higher performance of the proposed CR-DWT approach over the state-of-the-art methods in the literature, in terms of both the accuracy and classifier complexity.

Original languageEnglish
Article number6890226
JournalProceedings - IEEE International Conference on Multimedia and Expo
Volume2014-September
Issue numberSeptmber
DOIs
Publication statusPublished - 3 Sep 2014
Externally publishedYes
Event2014 IEEE International Conference on Multimedia and Expo, ICME 2014 - Chengdu, China
Duration: 14 Jul 201418 Jul 2014

Keywords

  • collaborative representation
  • Image classification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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