A robust and fast CFAR ship detector based on median absolute deviation thresholding for SAR imagery in heterogeneous log-normal sea clutter

Madjidi, Hicham; Laroussi, Toufik; Farah, Faiçal

doi:10.1007/s11760-023-02513-2

Cited by 6 publications

(1 citation statement)

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“…However, in real situations different sea surfaces have different statistical properties, and objects that are not the ship targets can also affect the accurate modeling. Therefore, some improved CFAR algorithms have been proposed, one of which is based on the implementation of more optimal statistical distributions, such as Lognormal, Gamma, or Alpha distributions [5][6][7]. There is also one that utilizes wavelet transform and template matching [8,9] for ship target detection.…”

Section: Introductionmentioning

confidence: 99%

CSEF-Net: Cross-Scale SAR Ship Detection Network Based on Efficient Receptive Field and Enhanced Hierarchical Fusion

Zhang,

2024

Remote Sensing

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Ship detection using synthetic aperture radar (SAR) images is widely applied to marine monitoring, ship identification, and other intelligent maritime applications. It also improves shipping efficiency, reduces marine traffic accidents, and promotes marine resource development. Land reflection and sea clutter introduce noise into SAR imaging, making the ship features in the image less prominent, which makes the detection of multi-scale ship targets more difficult. Therefore, a cross-scale ship detection network for SAR images based on efficient receptive field and enhanced hierarchical fusion is proposed. In order to retain more information and lighten the weight of the network, an efficient receptive field feature extraction backbone network (ERFBNet) is designed, and the multi-channel coordinate attention mechanism (MCCA) is embedded to highlight the ship features. Then, an enhanced hierarchical feature fusion network (EHFNet) is proposed to better characterize the features by fusing information from lower and higher layers. Finally, the feature map is input into the detection head with improved bounding box loss function. Using SSDD and HRSID as experimental datasets, average accuracies of 97.3% and 90.6% were obtained, respectively, and the network performed well in most scenarios.

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