Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images

A fast CFAR ship detector based on the statistical modeling of sea clutter in SAR images is proposed. Typical CFAR detectors, like the double parameter model (DPM), assume a Gaussian sea clutter model and usually degrade the image resolution using target windows. The proposed detector works in a pixel-by-pixel fashion, adaptively selecting a decision threshold for every single patch the SAR image is divided into. Therefore, the presence of different-sized ships will not become an issue and they will be better characterized, allowing the extraction of the target information, which could be used for refocusing, feature extraction, modeling and classification, while maintaining the resolution of the SAR image. Sea clutter is studied using different statistical models, with the Generalized Gamma distribution presenting itself as the most suitable one. This model is used to characterize the sea clutter in the proposed detector. In order to showcase the robustness of the proposed detector, images acquired with SAR sensors working in different frequency bands are selected. Ship detections results show a good performance regardless of the sensor, the ship size and the sea state.