URBAN AREAS CHARACTERIZATION FROM POLARIMETRIC SAR IMAGES USING HIDDEN MARKOV MODEL

被引：0

作者：

He, Wenju ^{[1
]}

Jaeger, Marc ^{[1
]}

Hellwich, Olaf ^{[1
]}

机构：

[1] Berlin Univ Technol, Berlin, Germany

来源：

2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5 | 2009年

关键词：

Hidden Markov Models; Synthetic aperture radar; Buildings; Subaperture;

D O I：

10.1109/IGARSS.2009.5417397

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Scatterers in Synthetic Aperture Radar (SAR) images exhibit high dependence on scatterer-sensor orientations. This phenomenon is prevalent in urban areas This paper applies Hidden Markov Model (HMM) to characterize the dependence and model the variations with respect to orientation. Buildings in high resolution SAR images of urban areas are studied. Buildings regions are divided into several discrete classes according to their orientation angles. We model the variations of scatterers characteristics throughout the subapertures using HMM. Subapertures are generated using wavelet packet decomposition. The experimental results show that HMM is efficient in building detection and orientation angle identification. HMMs trained using different feature sets are investigated The evolution of scatterer states in subapertures are obtained from the HMM inference.

引用

页码：2778 / 2781

页数：4

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