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

Fingerprint

Image classification
image classification
Glossaries
Remote sensing
learning
remote sensing
Atoms
method
dictionary

Keywords

  • classification
  • dictionary learning
  • Remote sensing
  • SPM

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
Yang, Michael Ying ; Jiang, Tao ; Al-Shaikhli, Saif ; Rosenhahn, Bodo. / A novel dictionary learning method for remote sensing image classification. IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Milan : IEEE, 2015. pp. 4364-4367 (International Geoscience and Remote Sensing Symposium (IGARSS)).
@inproceedings{6e5ec39b0fac4fcb86782383b441d971,
title = "A novel dictionary learning method for remote sensing image classification",
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.",
keywords = "classification, dictionary learning, Remote sensing, SPM",
author = "Yang, {Michael Ying} and Tao Jiang and Saif Al-Shaikhli and Bodo Rosenhahn",
year = "2015",
month = "11",
day = "10",
doi = "10.1109/IGARSS.2015.7326793",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "4364--4367",
booktitle = "IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings",
address = "United States",

}

Yang, MY, 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., 7326793, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2015-November, IEEE, Milan, pp. 4364-4367, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7326793

A novel dictionary learning method for remote sensing image classification. / Yang, Michael Ying; Jiang, Tao; Al-Shaikhli, Saif; Rosenhahn, Bodo.

IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Milan : IEEE, 2015. p. 4364-4367 7326793 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November).

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

TY - GEN

T1 - A novel dictionary learning method for remote sensing image classification

AU - Yang, Michael Ying

AU - Jiang, Tao

AU - Al-Shaikhli, Saif

AU - Rosenhahn, Bodo

PY - 2015/11/10

Y1 - 2015/11/10

N2 - 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.

AB - 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.

KW - classification

KW - dictionary learning

KW - Remote sensing

KW - SPM

UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1109/IGARSS.2015.7326793

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2015/chap/yang_nov.pdf

U2 - 10.1109/IGARSS.2015.7326793

DO - 10.1109/IGARSS.2015.7326793

M3 - Conference contribution

AN - SCOPUS:84962615499

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4364

EP - 4367

BT - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings

PB - IEEE

CY - Milan

ER -

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