DocumentCode :
576331
Title :
A robust Evidential Fisher Discriminant for multi-temporal hyperspectral images classification
Author :
Hemissi, S. ; Farah, I.R. ; Ettabaa, K. Saheb ; Solaiman, B.
Author_Institution :
RIADII Lab., Campus Univ. Manouba, Manouba, Tunisia
fYear :
2012
fDate :
22-27 July 2012
Firstpage :
4275
Lastpage :
4278
Abstract :
This paper develops a noise-robust processing method which can be used to enhance the classification of remotely sensed hyperspectral images. The method first illustrates the benefit of boosting the classical classifiers by exploiting the capability of belief functions. The evidential approach is adopted to produce a map which is approximately insensitive to the noise accompanying the original hyperspectral data-set. Then, a new Evidential Kernel Fisher Discriminant is proposed by using a modified version of the Expectation-Maximization (EM) algorithm. An experimental comparison of the proposed approach with other classical methods is conducted using both synthetic and real hyperspectral data collected by the HYPERION sensor. Our experiments reveal that both classification and unmixing process can benefit from the proposed aggregated approach, remarkably, when the noise level present in the original hyperspectral series is propositionally high.
Keywords :
expectation-maximisation algorithm; geophysical image processing; image classification; image enhancement; remote sensing; statistical analysis; EM algorithm; HYPERION sensor; belief function capability; expectation-maximization algorithm; hyperspectral data-set; multitemporal hyperspectral images classification; noise level; noise-robust processing method; remotely sensed hyperspectral image classification enhancement; robust evidential kernel Fisher discriminant; unmixing process; Hyperspectral imaging; Kernel; Noise; Training; Vectors; Vegetation mapping; Belief functions; EM Algorithm; Hyperspectral time series; Kernel Fisher Discriminator (KFD);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2012.6351723
Filename :
6351723
Link To Document :
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