An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery

Abstract

Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships’ small size and confusable detail feature. This article proposes a novel anchor-free detection method composed of two modules to deal with these problems. First, for the lack of detailed information on small ships, we suggest an adaptive feature-encoding module (AFE), which gradually fuses deep semantic features into shallow layers and realizes the adaptive learning of the spatial fusion weights. Thus, it can effectively enhance the external semantics and improve the representation ability of small targets. Next, for the foreground–background imbalance, the Gaussian-guided detection head (GDH) is introduced according to the idea of soft sampling and exploits Gaussian prior to assigning different weights to the detected bounding boxes at different locations in the training optimization. Moreover, the proposed Gauss-ness can down-weight the predicted scores of bounding boxes far from the object center. Finally, the effect of the detector composed of the two modules is verified on the two SAR ship datasets. The results demonstrate that our method can effectively improve the detection performance of small ships in datasets.

Keywords

• ship object detection
• deep learning
• remote sensing imagery
• feature extraction
• object sampling