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.

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A major task in any discrimination scenario requires the collection and validation of as many examples as possible. Depending on the type of data this can be a time consuming process, especially when dealing with large remote sensing data such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative Adversarial Networks pit a generating and discriminating network against each other. A generator tries to create samples that are indistinguishable from real data whereas the discriminator tries to identify whether a sample is real or generated. Information Maximizing Generative Adversarial Network extend this idea by extracting untangled latent variables as part of the discrimination process which help to classify the data in terms of categories/classes and properties such as ship rotation. Despite the limited size and class distribution of the dataset, the paper showed that the trained network was able to generate convincing samples from the three given classes as well as create a discriminator that performs similarly to state-of-the-art ship discrimination methods despite using no labels for training.

Synthetic Aperture Radar images is a proven technology that can be used to detect ships at sea which have no active transponders (commonly referred to as dark targets). Various methods have been proposed that process SAR images to monitor these targets. In this paper, we propose a novel ship detection method for Advanced Synthetic Aperture Radar imagery that combines a Constant False Alarm Rate ship prescreening method with a Haar-like feature cascade classifier. Experimental results indicate that this configuration provides a ship detection accuracy above 88% and half the False Alarm Rate of the traditional Constant False Alarm Rate method.