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.
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.
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