A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area ExtractionReport as inadecuate




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1 AYIN - Models of spatio-temporal structure for high-resolution image processing CRISAM - Inria Sophia Antipolis - Méditerranée

Abstract : 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.

Keywords : High resolution SAR TerraSAR-X classification texture : multinomial logistic unsupervised learning Classification EM





Author: Koray Kayabol - Josiane Zerubia -

Source: https://hal.archives-ouvertes.fr/



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