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Ship detection and classification from overhead imagery

This paper presents a sequence of image-processing algorithms suitable for detecting and classifying ships from nadir panchromatic electro-optical imagery. Results are shown of techniques for overcoming the presence of background sea clutter, sea wakes, and non-uniform illumination. Techniques are presented to measure vessel length, width, and direction-of-motion. Mention is made of the additional value of detecting identifying features such as unique superstructure, weaponry, fuel tanks, helicopter landing pads, cargo containers, etc. Various shipping databases are then described as well as a discussion of how measured features can be used as search parameters in these databases to pull out positive ship identification. These are components of a larger effort to develop a low-cost solution for detecting the presence of ships from readily-available overhead commercial imagery and comparing this information against various open-source ship-registry databases to categorize contacts for follow-on analysis.