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A comparison of texture and amplitude based unsupervised SAR image classifications for urban area extraction

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2 Author(s)
Koray Kayabol ; Ayin Research Team, INRIA Sophia Antipolis Mediterranee, 2004 route des Lucioles, BP93, 06902 Cedex, France ; Josiane Zerubia

We compare the performance of the texture and the amplitude based mixture density models for urban area extraction from high resolution Synthetic Aperture Radar (SAR) images. We use an Auto-Regressive (AR) model with t-distribution error for the textures and a Nakagami density for the amplitudes. We exploit a Multinomial Logistic (MnL) latent class label model as a mixture density to obtain spatially smooth class segments. We combine the Classification EM (CEM) algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL).We test our algorithm on TerraSAR-X data provided by DLR/DFD.

Published in:

2012 IEEE International Geoscience and Remote Sensing Symposium

Date of Conference:

22-27 July 2012