Outlier-Robust Superpixel-Level CFAR Detector With Truncated Clutter for Single Look Complex SAR Images

Li, Tao; Peng, Dongliang; Shi, Sai-Nan

doi:10.1109/jstars.2022.3187516

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…[7] proposes an automatic-identification-system (AIS) data aided CFAR detector for multiple-target detection in SAR image. Considering the capture effect from interfering outliers which can result in the performance degradation of the CFAR detector, [8] explores a statistical indicator analysis of the impacts of the complex signal kurtosis on the decision of truncation depth to guarantee the true clutter samples. In [9], the ship detection performance of different CFAR models is analyzed and compared by the indicators of CFAR target detection loss and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Curvature-based Feature Representation for Ship Detection in SAR Image

Chen,

Yang

2024

PIER Letters

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This article aims to exploit Ricci tensor with certain geometric properties which are used for feature representation and ship detection in synthetic aperture radar (SAR) image. The proposed method is composed of the following key points. Firstly, Riemannian metrics on the Gamma manifold are constructed based on the family of Gamma density functions. Secondly, direct calculation gives the Ricci tensor of Gamma manifold, where the curvature tensor resorts to the torsion-free affine connection. Thirdly, a general scheme for Zermelo navigation problem on the Riemannian manifold is addressed, and the solution of the navigation problem is proposed. Fourthly, feature representation problems are formulated as certain forms of Finsler metric of Randers type, indicating a joint framework for extracting low-dimensional features with closed-form solutions. Comprehensive experiments on real SAR image data sets demonstrate the effectiveness of the proposed method against compared state-of-the-art detection approaches.

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Section: Introductionmentioning

confidence: 99%

Curvature-based Feature Representation for Ship Detection in SAR Image

Chen,

Yang

2024

PIER Letters

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“…A new SP-level CFAR detector was designed by Pappas et al, which could not only reduce the false-alarm probability of ship target detection but also better maintain the ship shapes [19]. Moreover, to improve ship-detection accuracy, SP-level CFAR detectors are widely used in SAR ship-detection tasks [20][21][22]. Due to the influence of adjacent targets and side lobes of ships in multi-target environments, clutter modeling is inaccurate and the target-detection performance is poor.…”

Section: Introductionmentioning

confidence: 99%

Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images

Pan

Yang

et al. 2023

Remote Sensing

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Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.

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“…Its performance depends on accurate estimation of clutter. Researchers have proposed various clutter statistical distributions, such as distributions of K, gamma, and complex signal kurtosis(CSK) [7], [8], [9]. However, as the SAR image resolution improves and the backgrounds become more complex, CFAR algorithm is easy to estimate the distribution parameters of the target incorrectly, leading to a decrease in detection performance.…”

Section: Introductionmentioning

confidence: 99%

SPANet: A Self-Balancing Position Attention Network for Anchor-Free SAR Ship Detection

Chang

Jihua

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters. This results in poor robustness, which makes it difficult to effectively balance the detection accuracy and speed. To solve these problems, a novel anchor-free SAR ship detection algorithm based on self-balancing position attention is proposed. First, a lightweight feature extraction backbone (GhostVS-Net) is designed by FOCUS, Ghost, and separable convolution modules to extract feature information. This helps to extract the contour features of ships and suppress unrelated interference, making it more suitable for the scattering characteristics of SAR images. Second, a self-balancing position attention (SBPA) module is designed, which balances the local image features under multiple receptive fields, and aggregates the global position information and spatial context information. The proposed SBPA module considers the characteristics of background, scale, and distribution of SAR images to obtain greatly improved positioning accuracy. Finally, the feature pyramid network (FPN) is applied to fuse the scale context information, and further improve the accuracy of detection. Experimental results on the SSDD and HRSID datasets show that the proposed network bears feature of accurate detection and strong robustness, with the mean average precision (mAP) reaches 99.72% and 95.30%, which reveals that it outperforms all current state-of-the-art algorithms with super high performances.

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Outlier-Robust Superpixel-Level CFAR Detector With Truncated Clutter for Single Look Complex SAR Images

Cited by 10 publications

References 37 publications

Curvature-based Feature Representation for Ship Detection in SAR Image

Curvature-based Feature Representation for Ship Detection in SAR Image

Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images

SPANet: A Self-Balancing Position Attention Network for Anchor-Free SAR Ship Detection

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