Maritime vessel recognition in degraded satellite imagery

When object recognition algorithms are put to practice on real-world data, they face hurdles not always present in experimental situations. Imagery fed into recognition systems is often degraded by noise, occlusions, or other factors, and a successful recognition algorithm must be accurate on such data. This work investigates the impact of data degradations on an algorithm for the task of ship classification in satellite imagery by imposing such degradation factors on both training and testing data. The results of these experiments provide lessons for the development of real-world applications for classification algorithms.

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