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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)