İnce, TürkerKiranyaz S.Gabbouj, Moncef2023-06-162023-06-1620109.78E+121051-4651https://doi.org/10.1109/ICPR.2010.1051https://hdl.handle.net/20.500.14365/35542010 20th International Conference on Pattern Recognition, ICPR 2010 -- 23 August 2010 through 26 August 2010 -- Istanbul -- 82392This paper proposes an evolutionary RBF network classifier for polarimetric synthetic aperture radar ( SAR) images. The proposed feature extraction process utilizes the full covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/?/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. An experimental study is performed using the fully polarimetric San Francisco Bay data set 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 to the Wishart and a recent NN-based classifiers demonstrate the effectiveness of the proposed algorithm. © 2010 IEEE.eninfo:eu-repo/semantics/closedAccessAirborne SARClassification resultsConfusion matricesData setsExperimental studiesFeature spaceGray level co-occurrence matrixPolarimetric SARPolarimetric synthetic aperture radarsRBF NetworkSan Francisco BayTexture featuresClassifiersCovariance matrixFeature extractionImaging systemsPolarimetersPolarographic analysisRadial basis function networksSynthetic aperture radarPrincipal component analysisConference Object10.1109/ICPR.2010.10512-s2.0-78149471731