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

2 Citations (Scopus)

Abstract

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
PublisherIEEE
Pages4364-4367
Number of pages4
ISBN (Electronic)9781479979295
DOIs
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
http://www.igarss2015.org/

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2015-November

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Abbreviated titleIGARSS 2015
CountryItaly
CityMilan
Period26/07/1531/07/15
Internet address

Keywords

  • classification
  • dictionary learning
  • Remote sensing
  • SPM

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  • Cite this

    Yang, M. Y., Jiang, T., Al-Shaikhli, S., & Rosenhahn, B. (2015). A novel dictionary learning method for remote sensing image classification. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 4364-4367). [7326793] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November). Milan: IEEE. https://doi.org/10.1109/IGARSS.2015.7326793