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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
Publication Date: 2014/03/01
Online ISSN: 1745-1345
Print ISSN: 0916-8516
Type of Manuscript: PAPER
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.