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 language | English |
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Title of host publication | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings |
Place of Publication | Milan |
Publisher | IEEE |
Pages | 4364-4367 |
Number of pages | 4 |
ISBN (Electronic) | 9781479979295 |
DOIs | |
Publication status | Published - 10 Nov 2015 |
Event | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015: Remote Sensing: Understanding the Earth for a Safer World - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015 http://www.igarss2015.org/ |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2015-November |
Conference
Conference | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 |
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Abbreviated title | IGARSS 2015 |
Country/Territory | Italy |
City | Milan |
Period | 26/07/15 → 31/07/15 |
Internet address |
Keywords
- classification
- dictionary learning
- Remote sensing
- SPM