Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Xu, Lu; Zhang, Hong; Wang, Chao; Zhang, Bo; Tian, Sirui

doi:10.3390/rs8090751

Cited by 16 publications

(10 citation statements)

References 34 publications

(27 reference statements)

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“…AIS data, particularly the data for archived images, acquired synchronously with satellites is difficult to obtain. Therefore, as in previous studies [13,[22][23][24][47][48][49], some images used in this study were labeled with experience and expert knowledge through visual interpretation. Since ship characteristics (including ship superstructure configuration, orientation of a ship with respect to radar beam, ship size, material from which the ship is made) affect the observation (e.g., intensity and geometry) in SAR images and the detectability of ships followed by [8].…”

Section: Trainingmentioning

confidence: 99%

“…Another is to use the correlation of the size of the ships between SAR images and AIS data [58]. Moreover, when it is difficult to obtain validation data, the only way to obtain labeled targets is based on experience and expert knowledge [13,[22][23][24][47][48][49]. Although AIS information was used to verify the labeled dataset over several sub-areas, it is unfortunate that not all labeled ships in this study were supported by AIS data.…”

Section: Validationmentioning

confidence: 99%

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Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

et al. 2019

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Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization.

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Section: Trainingmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

et al. 2019

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“…Experiments on a real VideoSAR footage are carried out to verify the effectiveness of the proposed method. This footage is captured in Kirtland Air Force Base and published by Sandia National Laboratories [2], for which we are thankful. The parameters are set as follow: the footage contains 900 frames and owns a framerate of 29.9 Hz.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Video Synthetic Aperture Radar (VideoSAR) is a novel microwave detection technology proposed by researchers from Sandia National Laboratories, which combines SAR imaging and video techniques together. This novel technique enables continuous surveillance over a specific area, thus is regarded as an important mean of real-time battlefield reconnaissance and continuous earth observation [1][2][3]. By performing high-framerate and high-resolution imaging of the ground area, one could obtain valuable information, especially the motion status of moving targets [4].…”

Section: Introductionmentioning

confidence: 99%

Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information

Dong

et al. 2019

Remote Sensing

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VideoSAR (Video Synthetic Aperture Radar) technology provides an important mean for real-time and continuous earth observation, whereas the ever-changing scattering characteristics may destroy the accuracy of target motion perception and bring in massive false alarms subsequently. False alarms emerge easily in the edge region for its sharper variations of the scattering characteristics. Utilizing the gradient difference between the target shadow edge and other edge regions in the image, this letter proposes a VideoSAR false alarm reduction method based on gradient-weighted edge information. By considering the reasonable gradient and area of the overlapping edge region between changing region and background, this method could reduce the amount of false alarms ( P f a = 18 . 4 % ) and retain the correct shadow of moving target ( P d = 74 . 8 % ). Experiments on a real footage verify the excellent effect of the proposed method.

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“…The most frequently used ship detection algorithm is the intensity-based constant false alarm rate (CFAR) method with a suitable probability density function (PDF) for the background clutter model [6,7,8]. Other methods are based on the polarimetric analysis [9,10,11] and multi-look cross-correlation [12,13], etc. However, when ships are in heavy background noise, the task becomes challenging.…”

Section: Introductionmentioning

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.

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Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Cited by 16 publications

References 34 publications

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

Suppressing False Alarm in VideoSAR viaGradient-Weighted Edge Information

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

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