Optical and Synthetic Aperture Radar Image Fusion for Ship Detection and Recognition: Current state, challenges, and future prospects

Zhang, Zenghui; Zhang, Limeng; Wu, Juanping; Guo, Weiwei

doi:10.1109/mgrs.2024.3404506

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2024

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Cited by 5 publications

References 240 publications

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Revolutionizing Pan Sharpening in Remote Sensing with Cutting-Edge Deep Learning Optimization

Kaur

2024

2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

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Revolutionizing Pan Sharpening in Remote Sensing with Cutting-Edge Deep Learning Optimization

Kaur

2024

2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

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OptiSAR-Net: A Cross-Domain Ship Detection Method for Multisource Remote Sensing Data

Dong,

Feng,

Tang

2024

IEEE Trans. Geosci. Remote Sensing

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A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

Liu,

Luo,

2024

Remote Sensing

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Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches.

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Optical and Synthetic Aperture Radar Image Fusion for Ship Detection and Recognition: Current state, challenges, and future prospects

Cited by 5 publications

References 240 publications

Revolutionizing Pan Sharpening in Remote Sensing with Cutting-Edge Deep Learning Optimization

Revolutionizing Pan Sharpening in Remote Sensing with Cutting-Edge Deep Learning Optimization

OptiSAR-Net: A Cross-Domain Ship Detection Method for Multisource Remote Sensing Data

A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

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