A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

Abstract

Synthetic aperture radar (SAR) has emerged as a vital tool for ship monitoring due to its all-weather, all-day high-resolution imaging capabilities. In practical operations, the wide coverage and sparse ship distribution in very large full-scene SAR images pose challenges in terms of low efficiency and high false alarm rates. Traditional methods perform poorly in complex scenarios, while deep learning (DL) methods have high computation cost. This study proposes a fast progressive detection algorithm for ship targets in large SAR images, combining the advantages of traditional Non-DL methods and DL approaches. First, at a global scale, image preprocessing operations based on traditional methods are designed to quickly extract candidate regions. Then, at regional scale, an oriented ship detector is designed for refined ship detection within candidate regions. Finally, at individual-target scale, a false alarm discrimination network is constructed to further remove false alarms. Experimental results on GF-3 full-scene SAR images demonstrate that the proposed method can achieve minutes-level detection efficiency in images of billion-pixel-level size, while achieving high detection accuracy.