Automatic ship detection with SAR imagery is one of the important applications with SAR data

and it is widely used in many areas

such as fishery monitoring

environment protection

coastal security and military application.A large amount of ship targets detection researches using SAR data have been conducted since 1980s

and many detection techniques have been proposed.However

most of the traditional ship detection methods can be affected by the SAR speckle and background clutter

and many false alarms may be introduced in the detection results.To improve the ship detection capability

a new ship detection method

which is based on the gravity enhancement

is proposed in this paper.Initially

the targets pixels are enhanced by the interactions between themselves and their neighbors

and it is proved that SAR speckle and background clutter will be suppressed after the enhancement as well as the contrasts between targets and clutter have been greatly increased.Then

as the enhanced SAR imagery may not satisfy the homogenous hypothesis

an improved K-CFAR detector based on an adaptive segmentation algorithm is applied to the enhance imagery.The adaptive segmentation algorithm segments the whole marine area into a series of homogenous sub-images by the homogenous test

and the improved K-CFAR detector

which can adaptively choose probability density function（pdf）

will be applied to each sub-image to detect the ship targets.Finally

some Radarsat-1 data and Envisat ASAR data are used to validate the algorithm mentioned in this paper.Experimental results demonstrate that the ship detection algorithm based on gravity enhancement can effectively enhance ship targets as well as suppress speckle and clutter

therefore

detection rate will be increased and the false alarm rate will be reduced.