Automatic ship target classification based on aerial images

Abstract

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.

Variational approximate inferential probability generative model for ship recognition using remote sensing data

“…In view of the above-mentioned facts, N = 6, ARC code is C= [4,8,10,10,9,7] in this paper, we just take C 1 and C 6 , namely, F 47 = C 1 , F 48 = C 6 . Considering the majority of ship target, which bow and stern parts have occupied about 1/3 ∼ 1/8 of the whole ship, so usually the divided number is 3 ≤ N ≤ 8, specifically, N = 6.…”

“…Besides, based on rough set theory [6], Du et al [7] used the idea of attributes reduction to compute the significance weight of each candidate feature and select valid recognition features. Moreover, the improved Zernike moment invariant was used to recognize large warship in air remote sensing image [8]. In addition, an efficient and accurate face detection using feature selection was proposed in [9].…”

Multi-evidence fusion recognition of ship targets in sea battlefield’s remote sensing images

“…Using optical remote sensing image to recognize ship targets becomes possible. For example, Zhang [4] use the support vector machine technology to recognize the ship targets in Quick Bird satellite data, Lan et al [5] propose a modified Zernike moments model for recognizing the large warship in aerial images.…”

A Remote Sensing Ship Recognition Using Random Forest

“…Whereas SVM method performs well in dual-class problems and poorly in the multiclass classification tasks. The improved Zernike moment was employed to identify large military vessels in remote sensing image by Lan et al [2]. Chun used the idea of attributes reduction to compute the significance weight of each candidate feature and select valid recognition features [3].…”