Automatic ship target classification based on aerial images

As the important reconnaissance and offensive weapon in future battlefield, Micro Aerial Vehicle (MAV) is applied more and more widely in civil and military field. In the sea battlefield, ship classification applied to MAV could effectively realize signals collection, force protection and strike to ship targets. At present, methods of ship classification are mostly based on signals from radar, infrared or ultrasonic. However, because of large volume and complex equipments, these methods can't meet the requirement of MAV. Thus, ship classification based on visible sensor is chosen and it could solve volume and weight limits of MAV. In order to realize ship classification in MAV, ship classification based on aerial images is first proposed and an effective robust algorithm for classification based on modified Zernike moment invariants is proposed in this paper. The task of classification is that the ships are classified into two categories, aircraft carrier and chaser. The experimental results show that the correct classification rate is more than 92% and the algorithm proposed is effective to solve classification problem for ship targets in MAV.