Abstract
A method for synthetic aperture radar (SAR) image classification in urban areas based on modified Unit-linking pulse coupled neural networks (Unit-linking PCNN) and texture feature is presented. Unit-linking PCNN is modified to be two levels in order to make it classify more classes. The primary level corresponds to determining the initial threshold value of the secondary level, and in the secondary level, the similar neurons are captured using Unit-linking PCNN. Because of the imaging characteristic of SAR building areas, the texture feature of the neuron’s n ×n window image is used as the input pulse signal. Experimental results show that the proposed method is effective.
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Wang, R., Song, J., Zhang, X., Wu, Y. (2011). SAR Image Classification in Urban Areas Using Unit-Linking Pulse Coupled Neural Network. In: Jin, D., Lin, S. (eds) Advances in Multimedia, Software Engineering and Computing Vol.1. Advances in Intelligent and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25989-0_7
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DOI: https://doi.org/10.1007/978-3-642-25989-0_7
Publisher Name: Springer, Berlin, Heidelberg
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