Automatic classification of ships from infrared (FLIR) images

The aim of the research presented in this paper is to find out whether automatic classification of ships from Forward Looking InfraRed (FLIR) images is feasible in maritime patrol aircraft. An image processing system has been developed for this task. It includes iterative shading correction and a top hat filter for the detection of the ship. It uses a segmentation algorithm based on the gray value distribution of the waves and the Hough transform to locate the waterline of the ship. A model has been developed to relate the size of the ship and the angle between waterline and horizon in image coordinates, to the real-life size and aspect angle of the ship. The model uses the camera elevation and distance to the ship. A data set was used consisting of two civil ships and four different frigates under different aspect angles and distances. From each of these ship images, 32 features were calculated, among which are the apparent size, the location of the hot spot and of the superstructures of the ship, and moment invariant functions. All features were used in feature selection processing using both the Mahalanobis and nearest neighbor (NN) criteria in forward, backward, and branch & bound feature selection procedures, to find the most significant features. Classification has been performed using a k-NN, a linear, and a quadratic classifier. In particular, using the 1-NN classifier, good results were achieved using a two-step classification algorithm.