A novel dictionary learning method for remote sensing image classification

Michael Ying Yang, Tao Jiang, Saif Al-Shaikhli, Bodo Rosenhahn

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

3 Citations (Scopus)


With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Most classification methods focus on various combination of features and ignore the similarities between different categories. In this paper we present a modification by combining ScSPM [1] with a dictionary learning method DL-COPAR [2], which separates the particularity and commonality atoms of class-specific sub-dictionaries. With this over-complete dictionary, the sparse representation of a query image can be specified to capture salient and unique properties. Experimental results on two remote sensing datasets show that, this modification achieves state-of-the-art classification accuracy, when merely SIFT feature is applied.

Original languageEnglish
Title of host publicationIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Place of PublicationMilan
Number of pages4
ISBN (Electronic)9781479979295
Publication statusPublished - 10 Nov 2015
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015: Remote Sensing: Understanding the Earth for a Safer World - Milan, Italy
Duration: 26 Jul 201531 Jul 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Abbreviated titleIGARSS 2015
Internet address


  • classification
  • dictionary learning
  • Remote sensing
  • SPM

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