Ship recognition and classification in electro-optical satellite imagery is a challenging problem with important military applications. The problem is similar to that of face recognition, but with many unique considerations. A ship's appearance can vary dramatically from image to image depending on factors such as lighting condition, sensor angle, and ocean state, and there is often wide variation between ships of the same class. Collecting and labeling sufficient training data is another challenge. We consider how appropriate feature selection and description can assist in addressing these challenges. Our proposed algorithm for vessel classification combines shape invariant features such as SIFT with a well known face recognition algorithm from the theory of sparse representation and compressive sensing. We demonstrate improved classification accuracy using invariant features at significant key points instead of random features to represent images. We also discuss how algorithms such as this are currently implemented to detect and classify ships and other objects in ocean imagery.
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