A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High-Resolution SAR Images

Qin, Xianxiang; Zhou, Shilin; Zou, Huanxin; Gao, Gui

doi:10.1109/lgrs.2012.2224317

Cited by 137 publications

(35 citation statements)

References 15 publications

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“…Besides, we assess the effectiveness of h detector compared with SVWIE and

T_{n o n - c i r}

. Finally, the proposed method performances are compared with those of VWIE [14], SVWIE, G

Γ

D-CFAR [33] and the state-of-the-art truncated satistics (TS)-CFAR [8]. …”

Section: Experiments and Resultsmentioning

confidence: 99%

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Zhao

Liu

2018

Sensors

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In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (GΓD) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

show abstract

“…Besides, we assess the effectiveness of h detector compared with SVWIE and

T_{n o n - c i r}

. Finally, the proposed method performances are compared with those of VWIE [14], SVWIE, G

Γ

D-CFAR [33] and the state-of-the-art truncated satistics (TS)-CFAR [8]. …”

Section: Experiments and Resultsmentioning

confidence: 99%

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Zhao

Liu

2018

Sensors

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show abstract

“…To verify the performance of the special design in ESPN and ASDN, separately, our method is evaluated by employing three configurations, namely, "Coupled-CNN E", "Coupled-CNN A", and "Coupled-CNN E A", where "E" and "A" indicate the improvements in ESPN and ASDN, respectively. CFAR-based multi-stage (CFAR-MS) detector developed by the authors in [35] is implemented by SNAP. FRCN is selected as another baseline to demonstrate the superiority of the proposed techniques.…”

Section: Baseline Methodsmentioning

confidence: 99%

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

Zhao

Guo

Zhang

et al. 2018

Sci. China Inf. Sci.

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Ship detection from synthetic aperture radar (SAR) imagery plays a significant role in global marine surveillance. However, a desirable performance is rarely achieved when detecting small and densely clustered ship targets, and this problem is difficult to solve. Recently, convolutional neural networks (CNNs) have shown strong detection power in computer vision and are flexible in complex background conditions, whereas traditional methods have limited ability. However, CNNs struggle to detect small targets and densely clustered ones that exist widely in many SAR images. To address this problem while preserving the good properties for complex background conditions, we develop a coupled CNN for small and densely clustered SAR ship detection. The proposed method mainly consists of two subnetworks: an exhaustive ship proposal network (ESPN) for ship-like region generation from multiple layers with multiple receptive fields, and an accurate ship discrimination network (ASDN) for false alarm elimination by referring to the context information of each proposal generated by ESPN. The motivation in ESPN is to generate as many ship proposals as possible, and in ASDN, the goal is to obtain the final results accurately. Experiments are evaluated on two data sets. One is collected from 60 wide-swath Sentinel-1 images and the other is from 20 GaoFen-3 (GF-3) images. Both data sets contain many ships that are small and densely clustered. The quantitative comparison results illustrate the clear improvements of the new method in terms of average precision (AP) and F 1 score by 0.4028 and 0.3045 for the Sentinel-1 data set compared with the multi-step constant false alarm rate (CFAR-MS) method. The values are verified as 0.2033 and 0.1522 for the GF-3 data set. In addition, the new method is demonstrated to be more efficient than CFAR-MS.

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“…Traditional SAR ship detection methods are mainly based on constant false alarm (CFAR) 10 , 11 and their improved algorithms, 12 – 14 which are typically dependent on the statistical distribution of sea clutter. This characteristic will lead to poor robustness for new SAR images, and these methods require land-ocean presegmentation, which will limit the speed of ship detection 15 .…”

Section: Introductionmentioning

confidence: 99%

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

Zhang

Yang

et al. 2023

J. Appl. Rem. Sens.

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With the development of synthetic aperture radar (SAR) technology, more SAR datasets with high resolution and large scale have been obtained. Research using SAR images to detect and monitor marine targets has become one of the most important marine applications. In recent years, deep learning has been widely applied to target detection. However, it was difficult to use deep learning to train an SAR ship detection model in complex scenes. To resolve this problem, an SAR ship detection method combining YOLOv4 and the receptive field block (CY-RFB) was proposed in this paper. Extensive experimental results on the SAR-Ship-Dataset and SSDD datasets demonstrated that the proposed method had achieved supreme detection performance compared to the state-of-the-art ship detection methods in complex scenes, whether they were in offshore or inshore scenes of SAR images.

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A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High-Resolution SAR Images

Cited by 137 publications

References 15 publications

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network

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