Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/1216
Title: | Evolutionary RBF classifier for polarimetric SAR images | Authors: | İnce, Türker Kiranyaz, Serkan Gabbouj, Moncef |
Keywords: | Polarimetric synthetic aperture radar Radial basis function network Particle swarm optimization Unsupervised Classification Multifrequency Decomposition |
Publisher: | Pergamon-Elsevier Science Ltd | Abstract: | In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/alpha/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier. (C) 2011 Elsevier Ltd. All rights reserved. | URI: | https://doi.org/10.1016/j.eswa.2011.09.082 https://hdl.handle.net/20.500.14365/1216 |
ISSN: | 0957-4174 1873-6793 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
239.pdf Restricted Access | 1.34 MB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
28
checked on Aug 14, 2024
WEB OF SCIENCETM
Citations
24
checked on Aug 14, 2024
Page view(s)
94
checked on Aug 19, 2024
Download(s)
4
checked on Aug 19, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.