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CFAR ship detection in SAR images based on lognormal mixture models

CFAR ship detection in SAR images based on lognormal mixture models,Yi Cui,Jian Yang,Yoshio Yamaguchi

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CFAR ship detection in SAR images based on lognormal mixture models  
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In this paper, we propose a new model, the lognormal mixture model (LMM), for characterizing the non-negative sea clutter in intensity/amplitude SAR images. By a change of variables, we show that the LMM is in fact equivalent to the Gaussian mixture model (GMM) in the log intensity/amplitude domain, and thus the parameters can be effectively estimated using the expectation-maximization (EM) method. Furthermore, we solve the threshold calculation problem by Newton's method which enables a fast convergence. Accordingly, Constant False Alarm (CFAR) ship detection algorithm is designed using the LMM, and its effectiveness is demonstrated with SIR-C/X SAR data.
Published in 2011.
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