Swarm Learning: Perception–Retrieval–Localization for Ship Detection From Synthetic Aperture Radar Remote Sensing Imagery

Most existing models for ship detection in synthetic aperture radar (SAR) remote sensing imagery perform the target independent detection, leading to insufficient information sensing for a single target. Thence, we propose swarm learning (SL) to ease such dilemma. Motivated by the human visual system (HVS), SL excavates information on ship swarm, no longer just an individual, which can enable a promising performance improvement. During SL, a novel progressive learning paradigm, namely, perception-retrieval-localization (PRL), is proposed. First, regions where ships exist are perceived immediately to decrease background interference, which is called a straightforward perception. We define such regions as a…

• FR
  Fundamental Research Funds for the Central Universities

  Award: XJ2025022601