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Land-Surface Classification with Unevenness and Reflectance Taken into Consideration

  • Akira Hirose
Part of the Studies in Computational Intelligence book series (SCI, volume 400)

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.

Keywords

Segmentation Result Neural Connection Complex Texture Rocky Area Hebbian Rule 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe University of TokyoTokyoJapan

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