Ship detection algorithm in SAR images based on Alpha-stable model

Abstract

This paper proposes a new ship detection algorithm based on Alpha-stable model for detection ships in the spaceborne synthetic aperture radar (SAR) images. The current operational ship detection algorithm is based on Constant False Alarm Rate (CFAR) method. The major shortcoming of this method is that it requires an appropriate model to describe statistical characteristic of background clutter. For multilook SAR images, the Gaussian model can be used. However, the Gaussian model is only valid when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian model often fails to describe background sea clutter. In this study, we replace Gaussian model with Alpha-stable model, which is widely used in the application of impulsive or spiky signal processing, to describe the background sea clutter in SAR images. Similar to the typical Two-parameter CFAR algorithm based on Gaussian distribution, we move a set of local windows through the image and finds bright pixels that are statistically different than the surrounding sea clutter. Several RADARSAT-1 images are used to validate this Alpha-stable model based algorithm. The experimental results show improvements of using Alpha-stable model over the Gaussian model.