Multi-scale saliency-based ship detection in SAR images

Qian, Jiechang; Yu, Ying; Bi, Fukun

doi:10.1049/icp.2021.0649

Cited by 1 publication

(1 citation statement)

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“…Considering the complexity of clutter features in high-resolution SAR images, Sun et al [ 25 ] proposed a two-step ship target detection method based on coarse-to-fine mechanism, which can enhance ship targets based on the gravity field method. In view of the influence of speckle noise in sea clutter, Qian et al [ 26 ] proposed a SAR ship target detection method based on multi-scale saliency analysis in frequency domain, which can achieve accurate target detection through frequency-domain multi-scale analysis and saliency information fusion, and has the advantage of low computational cost. Li et al [ 27 ] proposed a contrast-based superpixel target detection method which performs target superpixel enhancement processing based on weighted information entropy (WIE) according to the difference of gray intensity between target superpixels and clutter superpixels.…”

Section: Introductionmentioning

confidence: 99%

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism

Pan

Yang

et al. 2022

Sensors

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Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

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Section: Introductionmentioning

confidence: 99%