Efficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical in SAR-based ship detection. A flexible network configuration is possible within Highway Networks due to an adaptive gating mechanism which prevents gradient decay across many layers. This paper presents a very deep High Network configuration as a ship discrimination stage for SAR ship detection. It also presents a three-class SAR dataset that allows for more meaningful analysis of ship discrimination performances. The proposed method was tested on a this SAR dataset and had the highest mean accuracy of all methods tested at 96.67%. The proposed ship discrimination method also provides improved false positive classification compared to the other methods tested.

Abstract-The detection of ships at sea is a difficult task made more so by uncooperative ships, especially when using transponder based ship detection systems. Synthetic Aperture Radar imagery provides a means of observation independent of the ships cooperation and over the years a vast amount of research has gone into the detection of ships using this imagery. One of the most common methods used for ship detection in Synthetic Aperture Radar imagery is the Cell-Averaging Constant False Alarm Rate prescreening method. It uses a scalar threshold value to determine how bright a pixel needs to be in order to be classified as a ship and thus inversely how many false alarms are permitted. This paper presents by a method of converting the scalar threshold into a threshold manifold. The manifold is adjusted using a Simulated Annealing algorithm to optimally fit to information provided by the ship distribution map which is generated from transponder data. By carefully selecting the input solution and threshold boundaries, much of the computational inefficiencies usually associated with Simulated Annealing can be avoided. The proposed method was tested on six ASAR images against five other methods and had a reported detection accuracy of 85.2% with a corresponding false alarm rate of 1.01 × 10 −7 .

Abstract-The detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility and large areas of concern. Synthetic Aperture Radar imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard Constant False Alarm Rate prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using AdaBoost training on the classifier with an accuracy of 89.38% and false alarm rate of 1.47 × 10 −8 across a large swath Sentinel-1 and RADARSAT-2 newly created SAR dataset.

Regular surveillance of territorial sea areas is increasingly important for coastal nations as these marine areas are a valuable economic resource (e.g. for fisheries or oil extraction). The responsibility for the administration, law enforcement, environmental protection and sustainable management of this frontier can be very challenging as systematic surveillance of these areas is very costly and logistically cannot cover all areas all of the time. SAR data is very popular for ship detection as large areas can be observed within a single overpass. In this paper it is shown how ship detection using the classic CFAR algorithm can be improved by using historic LRIT data.