DocumentCode :
1893662
Title :
Feature selection and image classification using rough sets theory
Author :
Pessoa, Alex Sandro Aquiar ; Stephany, Stephan ; Fonseca, Leila Marcia Garcia
Author_Institution :
Postgrad. Program in Appl. Comput., Nat. Inst. for Space Res., Sao Jose dos Campos, Brazil
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
2904
Lastpage :
2907
Abstract :
Current generation of satellite imaging sensors include multispectral or even hyperspectral devices. The resulting multiple images that are acquired require new processing and analysis techniques. Image classification processing demands can be very high requiring feature/attribute selection in order to employ a minimum number of bands while keeping good classification accuracy. This work shows the use of the Rough Sets theory for multi-band image classification. This theory has a good and simple mathematical formalism and does not requires further informations such as the pertinence degree or the probability distribution in the classification process. The case study was performed with a 7-band Landsat 5 image showing the suitability of the feature selection approach and its potential to be employed in multi or hyperspectral image classification.
Keywords :
feature extraction; geophysical image processing; image classification; remote sensing; Landsat 5 image; attribute selection; feature selection; hyperspectral devices; hyperspectral image classification; multiband image classification; multispectral devices; rough set theory; satellite imaging sensors; Decision support systems; digital image processing; feature selection; rough sets theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6049822
Filename :
6049822
Link To Document :
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