Segmentation of SAR image using mixture multiscale ARMA network

Authors:
Haixia Xu;Zheng Tian;Fan Meng
Affiliations:
Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, P.R. China;Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, P.R. China;Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

A mixture multiscale autoregressive moving average (ARMA) network is proposed for unsupervised segmentation of synthetic aperture radar (SAR) image. The network combines the multiscale analysis (MA) method and the feedforward artificial neural network (FANN), thus maintains some of the characteristics of the MA method and the FANN respectively. A corresponding learning algorithm is derived based on the Akaike's information criterion (AIC) and genetic algorithm (GA). Experimental results on SAR images are shown to validate the presented network and learning algorithm.