\r\nin tasks related to sea traffic control, fishery management and ship

\r\nsearch and rescue. Although it has traditionally been carried out

\r\nby patrol ships or aircrafts, coverage and weather conditions and

\r\nsea state can become a problem. Synthetic aperture radars can

\r\nsurpass these coverage limitations and work under any climatological

\r\ncondition. A fast CFAR ship detector based on a robust statistical

\r\nmodeling of sea clutter with respect to sea states in SAR images

\r\nis used. In this paper, the minimum SNR required to obtain a

\r\ngiven detection probability with a given false alarm rate for any

\r\nsea state is determined. A Gaussian target model using real SAR

\r\ndata is considered. Results show that SNR does not depend heavily

\r\non the class considered. Provided there is some variation in the

\r\nbackscattering of targets in SAR imagery, the detection probability

\r\nis limited and a post-processing stage based on morphology would

\r\nbe suitable.","references":"[1] J. C. Curlander and R. N. McDonough, Synthetic Aperture Radar:\r\nSystems and Signal Processing. Wiley-Interscience, 1991.\r\n[2] R. Bamler, \u201cPrinciples of synthetic aperture radar,\u201d Surveys in\r\nGeophysics, vol. 21, pp. 147\u2013157, 2000. [3] V. Anastassopoulos, G. A. Lampropoulos, A. Drosopulos, and M. Rey,\r\n\u201cHigh resolution radar clutter statistics,\u201d IEEE Transactions on\r\nAerospace and Electronic Systems, vol. 35, no. 1, pp. 43\u201360, January\r\n1999.\r\n[4] J. Carretero-Moya, J. Gismero-Menoyo, A. B. del Campo, and\r\nA. Asensio-L\u00b4opez, \u201cStatistical analysis of a high-resolution sea-clutter\r\ndatabase,\u201d IEEE Transactions on Geoscience and Remote Sensing,\r\nvol. 48, no. 4, pp. 2024\u20132037, April 2010.\r\n[5] S. Chitroub, A. Houacine, and B. Sansal, \u201cStatistical characterisation and\r\nmodelling of sar images,\u201d Elsevier Signal Processing, vol. 82, no. 1, pp.\r\n66\u201392, 2002.\r\n[6] Y. Delignon, R. Garello, and A. Hillion, \u201cStatistical modelling of ocean\r\nsar images,\u201d IEE Proceedings on Radar, Sonar and Navigation, vol. 144,\r\nno. 6, pp. 348\u2013354, December 1997.\r\n[7] G. Gao, \u201cStatistical modeling of sar images: A survey,\u201d Sensors, vol. 10,\r\npp. 775\u2013795, 2010.\r\n[8] E. Kuruoglu and J. Zerubia, \u201cModeling sar images with a generalization\r\nof the rayleigh distribution,\u201d IEEE Transactions on Image Processing,\r\nvol. 13, no. 4, pp. 527\u2013533, April 2004.\r\n[9] J. Martin-de-Nicolas et al., \u201cStatistical Analysis of SAR Sea Clutter for\r\nClassification Purposes,\u201d Remote Sensing, vol. 6, no. 10, pp. 9379\u20139411,\r\n2014.\r\n[10] J. Neyman and E. S. Pearson, \u201cOn the problem of the most efficient test\r\nof statistical hypotheses,\u201d Springer New York, 1992.\r\n[11] J. Mart\u00b4\u0131n-de-Nicol\u00b4as et al., \u201cA Non-Parametric CFAR Detector Based\r\non SAR Sea Clutter Statistical Modeling,\u201d in IEEE International\r\nConference on Image Processing, 2015.\r\n[12] C. Wang et al., \u201cShip Detection for High-Resolution SAR Images Based\r\non Feature Analysis,\u201d IEEE Geoscience and Remote Sensing Letters,\r\nvol. 11, no. 1, pp. 119\u2013123, January 2014.\r\n[13] S. Brusch et al., \u201cShip Surveillance With TerraSAR-X,\u201d IEEE\r\nTransactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp.\r\n1092\u20131103, March 2011.\r\n[14] M. Martorella, F. Berizzi, D. Pastina, and P. Lombardo, \u201cExploitation\r\nof cosmo skymed sar images for maritime traffic surveillance,\u201d in IEEE\r\nRadar Conference, May 2011, pp. 113\u2013117.\r\n[15] D. Pastina, G. Battistello, and A. Aprile, \u201cChange detection based GMTI\r\non single channel SAR images,\u201d in European Conference on Synthetic\r\nAperture Radar, 2008.\r\n[16] C. J. Willis, \u201cTarget modelling for SAR image simulation,\u201d SPIE Remote\r\nSensing, 2014.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 119, 2016"}