An Effective Multi-Layer Attention Network for SAR Ship Detection

Abstract

The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.

CSEF-Net: Cross-Scale SAR Ship Detection Network Based on Efficient Receptive Field and Enhanced Hierarchical Fusion

“…To overcome multiple targets in SAR images, more than one approach has been presented. Suo et al [26] fused low-level spatial information with high-level semantic information across hierarchical levels to solve the problem of difficult detection due to large changes in ship size. Li et al [27] designed multiple pyramid modules, and each contained a different combination of convolutional layers.…”