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<p>The ability to locate and identify vessels of interest in satellite imagery plays a vital role in maintaining maritime security. Recent studies have demonstrated that convolutional neural networks can be used to automatically classify or detect ships in satellite images; however, this technique requires large amounts of training data and computational power that may not be readily available in an operational environment. We seek to show that the process of transfer learning can be used to adapt open source convolutional neural network architectures pre-trained on large datasets to Department of Defense-specific image classification and detection tasks. We test this hypothesis by retraining both the VGG-16 image classification architecture and a VGG-16 based Single Shot Detector on a dataset comprised of satellite images containing ships. We first examine the performance of these retrained networks on the single category task of classifying or detecting ships in satellite imagery. We then evaluate model performance on datasets in which a fraction of the images contains blur and noise to simulate degraded satellite imagery. Finally, we test the models’ ability to distinguish between subcategories of ships. We show that transfer learning can be leveraged to reduce both the size of the training set and the training time required to produce an effective classification or detection model to meet the Department of Defense’s analysis needs.
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<br>Subjects: deep learning; machine learning; image classification; object detection; transfer learning; satellite imagery; single shot detector; convolutional neural network</div>
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Rice, Katherine</div>
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