CFAR-CNN Detector of Ships from SAR Image Using Generalized Gamma Distribution and Real Dataset

The detection and localization of ships is important and must be accurate and rapid. The Synthetic Aperture Radar (SAR) is optimal for ship detection. Generally, Constant False Alarm Rate (CFAR) algorithms are used to detect ships from a SAR image on the base of satellite remote sensing imaging. However, due to the rapid development of technology, the remote sensing data have shown the features of big-data. The analyze of big-data improves the accuracy and speed of the ship detection. Therefore, Deep Learning (DL) is recommended and exactly the Convolutional Neural Network (CNN) model has greatly improved the static image recognition performance. In this paper, to improve the accuracy and speed of the ship detection from SAR image, we introduce the CFAR-CNN detector. First, after modeling the sea clutter by the Generalized Gamma (GΓ) distribution, a CFAR global detector is applied. Then, to improve the accuracy of the previous results, a CNN local detector is applied. To this end, a real dataset is used to obtain the optimal CNN model. We have shown that this detector is rapid and tends toward ideality.