A novel method is presented for ship detection in synthetic aperture radar （SAR） images

which is based on the constant false alarm rate （CFAR） technique and considers the probability density function of sea clutter as Gaussian distribution. All possible ship targets are detected using an overall threshold

which is calculated using the analytic formula. Then a statistic filter is used to eliminate the false ship pixels. This method avoids complicated iteration

calculation of shape parameters and dichotomy threshold

and therefore its accuracy and computation speed are improved

which are demonstrated by the results.In the paper

the main ATR techniques for ship detection in SAR images are reviewed

which include the window filter method

self-adapting threshold method

pdf （probability density function） method and PNN （Probability Neural Network） mo-del. A novel method is then presented

which is based on CFAR technique and Gaussian distribution of sea surface clutter. In this method

CFAR operator is given based on Gaussian distribution （normal distribution）

and the statistic filter is introduced to eliminate the false ship pixels

finally the framework of the method is described. The X-SAR and ERS SAR images are used for the algorithm test. Parameters such as detection threshold

computation time

number of detected targets and target pixel numbers are chosen as parameters for comparison with other methods. Results and comparison show that the new method proposed in this paper has advantages of high accuracy and computation speed.