Squeeze-and-Excitation Laplacian Pyramid Network With Dual-Polarization Feature Fusion for Ship Classification in SAR Images
Tianwen Zhang, Xiaoling Zhang
Abstract
This letter proposes a squeeze-and-excitation Laplacian pyramid network with dual-polarization feature fusion (SE-LPN-DPFF) for ship classification in synthetic aperture radar (SAR) images. SE-LPN-DPFF offers three contributions: 1) dual-polarization (VV and VH) feature fusion (DPFF); 2) channel modeling by the squeeze-and-excitation (SE) to balance each polarization feature’s contribution; and 3) Laplacian pyramid network (LPN) to achieve multiresolution analysis (MRA). Extensive ablation studies can confirm the effectiveness of each contribution. Results on the three- and six-category OpenSARShip datasets reveal the state-of-the-art SAR ship classification performance.
Topics & Concepts
Computer scienceArtificial intelligenceFeature extractionPyramid (geometry)Computer visionFeature (linguistics)Dual (grammatical number)Pattern recognition (psychology)Synthetic aperture radarPolarization (electrochemistry)Remote sensingImage fusionGeologyImage (mathematics)PhysicsOpticsLiteraturePhysical chemistryLinguisticsArtPhilosophyChemistrySynthetic Aperture Radar (SAR) Applications and TechniquesUnderwater Acoustics ResearchAdvanced SAR Imaging Techniques