SRT-Net:Scattering Region Topology Network for Oriented Ship Detection in Large-Scale SAR Images: Scattering Region Topology Network for Oriented Ship Detection in Large-Scale SAR Images

Dece Pan, Xin Gao, Wei Dai, Jiamei Fu, Zhirui Wang, Xian Sun, Youming Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Synthetic aperture radar (SAR) ship detection plays an important role in the field of maritime security. However, certain unique imaging properties make it challenging to extract the shape features of ships, such as speckle noise and strong scattering interference from irrelevant objects. These factors result in inaccurate ship localization and obvious false alarms under complex large-scale inshore scenes. To address this issue, we propose the Scattering Region Topology Network (SRT-Net), which can dynamically capture the comprehensive global context and enhance the ship saliency. This is achieved through two key modules, namely the Scattering Region Topological Structure Pyramid (SRTP) and the Ship Saliency Enhancement (SSE) module. The former provides richer semantic information to distinguish the object from the background, while the latter offers an extra pixel-level classification task to guide accurate bounding box regression. Thanks to the guidance of richer information, the proposed method can achieve fewer false alarms and enhance location accuracy. Additionally, we introduce a Scale Feature Adaptive (SFA) loss to balance the attention to ships with various scales, which improves the robustness of multi-scale ship detection. The proposed method achieves state-of-the-art performance under complex inshore scenes, and its effectiveness is verified by experiments on a large-scale SAR ship detection dataset (LSSDD) and a public SAR ship detection dataset (SSDD+).

Original languageEnglish (US)
Article number5202318
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume62
DOIs
StateAccepted/In press - 2024

Keywords

  • Deep learning
  • Detectors
  • Feature extraction
  • graph convolution network
  • Marine vehicles
  • oriented ship detection
  • Radar polarimetry
  • Scattering
  • strong scattering interference
  • Synthetic aperture radar
  • synthetic aperture radar (SAR)
  • Task analysis
  • topological structure

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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