A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship Detection

Hao Shi, Bingqian Chai, Yupei Wang*, Liang Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.

Original languageEnglish
Article number5247
JournalRemote Sensing
Volume14
Issue number20
DOIs
Publication statusPublished - Oct 2022

Keywords

  • SAR ship detection
  • Swin Transformer
  • contour enhancement
  • deep learning
  • deformable attention

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Shi, H., Chai, B., Wang, Y., & Chen, L. (2022). A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship Detection. Remote Sensing, 14(20), Article 5247. https://doi.org/10.3390/rs14205247