Abstract
In this chapter, we describe an adaptive system to classify land surface by taking unevenness and reflectance into consideration. We deal with inter-ferograms on the basis of the complex-valued Markov random field (CMRF) model in statistics. We generate an adaptively segmented map in terms of the complex-valued texture of land-surface reflection by using the complex-valued self-organizing map (CSOM) that processes CMRF-based feature vectors.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hirose, A. (2012). Land-Surface Classification with Unevenness and Reflectance Taken into Consideration. In: Complex-Valued Neural Networks. Studies in Computational Intelligence, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27632-3_5
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DOI: https://doi.org/10.1007/978-3-642-27632-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27631-6
Online ISBN: 978-3-642-27632-3
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