Unsupervised Speckle Level Estimation of SAR Images Using Texture Analysis and AR Model

Bin XU  Yi CUI  Guangyi ZHOU  Biao YOU  Jian YANG  Jianshe SONG  

IEICE TRANSACTIONS on Communications   Vol.E97-B   No.3   pp.691-698
Publication Date: 2014/03/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E97.B.691
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Sensing
AR model,  equivalent number of looks (ENL),  synthetic aperture radar (SAR),  texture analysis,  

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In this paper, a new method is proposed for unsupervised speckle level estimation in synthetic aperture radar (SAR) images. It is assumed that fully developed speckle intensity has a Gamma distribution. Based on this assumption, estimation of the equivalent number of looks (ENL) is transformed into noise variance estimation in the logarithmic SAR image domain. In order to improve estimation accuracy, texture analysis is also applied to exclude areas where speckle is not fully developed (e.g., urban areas). Finally, the noise variance is estimated by a 2-dimensional autoregressive (AR) model. The effectiveness of the proposed method is verified with several SAR images from different SAR systems and simulated images.