Classification of Polarimetric SAR Images Using Evolutionary RBF Neural Networks

This 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/α/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.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Ince Turker, Serkan Kiranyaz, Gabbouj Moncef
• Keywords: Computer Vision, ICPR 2010, Level Co-occurrence Matrix, Polarimetric Synthetic Aperture, RBF Network Classifier