Abstract
A multiscale self-organizing mixture network (MSOMN) is proposed for learning mixture multiscale autoregressive model of synthetic aperture radar (SAR) imagery. The MSOMN combines the multiscale method, the Kullback-Leibler information metric, the stochastic approximation method, and the self-organizing map structure. Updating of the parameters is limited to a small neighborhood around the winner that is based on maximum posterior probability. The network possesses a simple structure, and yields fast convergence, which is confirmed by experimental results of SAR images.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wen, X. (2005). Classification of SAR Imagery Using Multiscale Self-organizing Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_49
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DOI: https://doi.org/10.1007/11427445_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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