Efficient Generation of Artificial Training DB for Ship Detection Using Satellite SAR Images

Abstract

In this article, we propose an effective scheme to generate an artificial training database (DB) to mitigate the deficiency in the amount of training DB for ship detection using satellite synthetic aperture radar (SAR) images. In the proposed scheme, SAR signatures of ship targets are first obtained from two different types of image: 1) Korea Multipurpose Satellite-5 images with automatic identification system information and 2) simulated SAR images that are acquired using electromagnetic numerical analysis. Then, the ship signatures are combined with sea clutter models to generate realistic SAR patches. For overall ship detection tasks (i.e., training and testing), the oriented bounding box concept was adopted to appropriately represent slender ship targets in SAR images. When compared with the rash collection of a large number of real SAR images, the proposed scheme is more efficient in terms of cost and time. Experimental results demonstrated that the proposed method can considerably aid in increasing the ship detection capacity of deep learning models. Thus, it is expected that the proposed scheme can be usefully exploited to solve the lack of the amount of training DB.

Keywords

• Electromagnetic numerical analysis (EMNA)
• satellite synthetic aperture radar
• ship detection
• synthetic aperture radar (SAR) remote sensing