Image segmentation is a fundamental step for SAR image based automatic target recognition (ATR). A method based on moment feature and multiple thresholds is proposed for moving and stationary target a cquisition and recognition (MSTAR) image segmentation. First

according to a comprehensive research of the statistics of MSTAR images

the mathematical description models for target regions

shadows

and background regions are constructed respectivedly. Then

the moment feature is defined

followed by the analysis of its basic properties. By transforming the image into moment feature space

the difference between target region and the other two types of regions is significantly enhanced. Finally

a strategy with multiple thresholds is constructed for the segmentation. The experiment results with MSTAR dataset indicate that the algorithm presented here has advantages not only on the noise robustness

but also on the segmentation effect

as well as the processing efficiency over the common-used methods

such as OTSU

fuzzy C-means(FCM)

Markov random field(MRF)

and constant false alarm rate (CFAR). Furthermore

this new method also performs well in the segmentation of MSTAR images with various scales and multiple targets.