首页|PSO-based fine polarimetric decomposition for ship scattering characterization

PSO-based fine polarimetric decomposition for ship scattering characterization

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Due to the inappropriate estimation and inadequate awareness of scattering from complex substructures within ships, a reasonable, reliable, and complete interpretation tool to characterize ship scattering for polarimetric synthetic aperture radar (PolSAR) is still lacking. In this paper, a fine polarimetric decomposition with explicit physical meaning is proposed to reveal and characterize the local-structure-related scattering behaviors on ships. To this end, a nine-component decomposition scheme is first established through incorporating the rotated dihedral and planar resonator scattering models, which makes full use of polarimetric information and comprehensively considers the complex structure scattering of ships. In order to reasonably estimation the scattering components, three practical scattering dominance principles as well as an explicit objective function are raised, and a particle swarm optimization (PSO)-based model inversion strategy is subsequently presented. This not only overcomes the underdetermined problem, but also improves the scattering mechanism ambiguity by circumventing the constrained estimation order. Finally, a ship indicator by linearly combining the output scattering contribution is further derived, which constitutes a complete ship scattering interpretation approach along with the proposed decomposition. Experiments carried out with real PolSAR datasets demonstrate that the proposed method adequately and objectively describes the scatterers on ships, which provides an effective way to ship scattering characterization. Moreover, it also verifies the feasibility of fine polarimetric decomposition in a further application with the quantitative analysis of scattering components.

PolSARFine polarimetric decompositionParticle swarm optimizationShip indicatorShip scattering characterizationMODEL

Wang, Junpeng、Quan, Sinong、Xing, Shiqi、Li, Yongzhen、Wu, Hao、Meng, Weize

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Natl Univ Def Technol

Inst Dataspace

2025

ISPRS journal of photogrammetry and remote sensing

ISPRS journal of photogrammetry and remote sensing

ISSN:0924-2716
年,卷(期):2025.220(Feb.)
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