Discriminating small extended targets at sea from clutter and other classes of boats in infrared and visual light imagery

Operating in a coastal environment, with a multitude of boats of different sizes, detection of small extended targets is only one problem. A further difficulty is in discriminating detections of possible threats from alarms due to sea and coastal clutter, and from boats that are neutral for a specific operational task. Adding target features to detections allows filtering out clutter before tracking. Features can also be used to add labels resulting from a classification step. Both will help tracking by facilitating association. Labeling and information from features can be an aid to an operator, or can reduce the number of false alarms for more automatic systems. In this paper we present work on clutter reduction and classification of small extended targets from infrared and visual light imagery. Several methods for discriminating between classes of objects were examined, with an emphasis on less complex techniques, such as rules and decision trees. Similar techniques can be used to discriminate between targets and clutter, and between different classes of boats. Different features are examined that possibly allow discrimination between several classes. Data recordings are used, in infrared and visual light, with a range of targets including rhibs, cabin boats and jet-skis