Phase calibration for ideal wideband chirp in satellite-based synthetic aperture radar

Kim, Kyeongrok; Kang, Junghwa; Kim, Jae-Hyun

doi:10.1016/j.icte.2021.09.007

Cited by 1 publication

(1 citation statement)

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“…In the field of ocean monitoring, Synthetic Aperture Radar (SAR) technology is particularly advantageous for detecting ship targets due to its all-weather, all-time surveillance capabilities [1]. Since ships are the primary mode of maritime transportation, monitoring their movements is crucial for the regulation and management of maritime traffic.…”

Section: Introductionmentioning

confidence: 99%

MSSD-Net: Multi-Scale SAR Ship Detection Network

Wang,

Xu,

Huang

et al. 2024

Remote Sensing

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In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network’s capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks.

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Section: Introductionmentioning

confidence: 99%