Automatic ship recognition robust against aspect angle changes and occlusions

We propose a novel automatic recognition method of ships in images produced by inverse synthetic aperture radar (ISAR). It has robustness against ship deformation due to changing aspect angles and loss of ship parts caused by occlusion. To deal with the deformation and the loss, we extract a feature vector from a ship in an ISAR image by Co-occurrence Histograms of Oriented Gradients (CoHOG). An ISAR ship image is divided into multiple blocks and CoHOG is extracted from pairs of quantized gradient orientations in each of the blocks. Quantized orientations are not changed by slight deformation of a ship. This property derives that CoHOG has robustness against the deformation due to changing aspect angles. Variation of CoHOG caused by occlusion is small since gradient orientations are changed in only blocks including occluded parts. On the other hand, combinations of pairs of orientations make dimension of CoHOG extremely high. We calculate a similarity between two ships using Random Ensemble Metric (REMetric), which is a metric learning method for a high dimensional feature space. It has multiple Support Vector Machines (SVM) learnt from randomly subsampled training data, and calculates a similarity between two vectors from results of SVM of these two vectors. Since SVM finds a hyperplane which has maximum margin between two classes, its classification performance is high even through dimension of a feature space is high. Through experiments with simulated ISAR ships images, we show our method has robustness against aspect angle changes and occlusion, and it has higher performance than a conventional method.