An Efficient Ship Target Integrated Imaging and Detection Framework (ST-IIDF) for Space-Borne SAR Echo Data

An Efficient Ship Target Integrated Imaging and Detection Framework (ST-IIDF) for Space-Borne SAR Echo Data

Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target info...

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Bibliographic Details
Main Authors: Can Su, Wei Yang, Yongchen Pan, Hongcheng Zeng, Yamin Wang, Jie Chen, Zhixiang Huang, Wei Xiong, Chunsheng Li
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
wide-area offshore scenarios
space-borne SAR
integrated imaging and detection framework
potential target area filtering
peak-valley detection method
dynamic quantization
Online Access:https://www.mdpi.com/2072-4292/17/15/2545
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Summary:Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target information acquisition tasks. Therefore, we propose a ship target integrated imaging and detection framework (ST-IIDF) for SAR oceanic region data. A two-step filtering structure is added in the SAR imaging process to extract the potential areas of ship targets, which can accelerate the whole process. First, an improved peak-valley detection method based on one-dimensional scattering characteristics is used to locate the range gate units for ship targets. Second, a dynamic quantization method is applied to the imaged range gate units to further determine the azimuth region. Finally, a lightweight YOLO neural network is used to eliminate false alarm areas and obtain accurate positions of the ship targets. Through experiments on Hisea-1 and Pujiang-2 data, within sparse target scenes, the framework maintains over 90% accuracy in ship target detection, with an average processing speed increase of 35.95 times. The framework can be applied to ship target detection tasks with high timeliness requirements and provides an effective solution for real-time onboard processing.
ISSN:2072-4292

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