Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier

Abstract

Ship detection on the SAR images for marine monitoring has a wide usage. SAR technology helps us to have a better monitoring over intended sections, without considering atmospheric conditions, or image shooting time. In recent years, with advancements in convolutional neural network (CNN), which is one of the well-known ways of deep learning, using image deep features has increased. Recently, usage of CNN for SAR image segmentation has been increased. Existence of clutter edge, multiple interfering targets, speckle and sea-level clutters makes false alarms and false detections on detector algorithms. In this letter, constant false alarm rate is used for object recognition. This algorithm, processes the image pixel by pixel, and based on statistical information of its neighbor pixels, detects the targeted pixels. Afterward, a neural network with hybrid algorithm of CNN and multilayer perceptron (CNN–MLP) is suggested for image classification. In this proposal, the algorithm is trained with real SAR images from Sentinel-1 and RADARSAT-2 satellites, and has a better performance on object classification than state of the art.