Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 4364-4367 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781479979295 |
| Publikationsstatus | Veröffentlicht - 10 Nov. 2015 |
| Veranstaltung | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italien Dauer: 26 Juli 2015 → 31 Juli 2015 |
Publikationsreihe
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Band | 2015-November |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
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2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. S. 4364-4367 7326793 (International Geoscience and Remote Sensing Symposium (IGARSS); Band 2015-November).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-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 - http://www.scopus.com/inward/record.url?scp=84962615499&partnerID=8YFLogxK
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 - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Y2 - 26 July 2015 through 31 July 2015
ER -