Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Pan, Zongxu; Liu, Lei; Qiu, Xiaolan; Lei, Bin

doi:10.3390/s17071578

Cited by 28 publications

(14 citation statements)

References 40 publications

(68 reference statements)

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“…The high-resolution SAR images can be applied for a multitude of scientific research ranging from geoscience to Earth system monitoring. GF-3 SAR images have been utilized for several applications, such as wind and wave retrieval [31] and vessel detection [32]. The SAR images enable full-time, all-weather ocean and land monitoring, disaster reduction, water conservancy, and meteorology.…”

Section: Data and The Study Areamentioning

confidence: 99%

Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms

Wang

Deng

et al. 2019

Journal of Sensors

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More and more synthetic aperture radar (SAR) satellites in orbit provide abundant data for remote sensing applications. In August 2016, China launched a new Earth observation SAR satellite, Gaofen-3 (GF-3). In this paper, we utilize a small stack of GF-3 differential interferograms to map land subsidence in Beijing (China) using the time-series SAR interferometry (InSAR) technique. The small stack of differential interferograms is generated with 5 GF-3 SAR images from March 2017 to January 2018. Orbit errors are carefully addressed and removed during differential InSAR (DInSAR) processing. Truncated singular-value decomposition (TSVD) is applied to strengthen the robustness of deformation rate estimation. To validate the results of GF-3 data, an additional deformation measurement using 26 Sentinel-1B images from March 2017 to February 2018 is carried out using the persistent scatterer interferometry (PSI) technique. By implementing a cross-comparison, we find that the retrieved results from GF-3 images and Sentinel-1 images are spatially consistent. The standard deviation of vertical deformation rate differences between two data stacks is 11.24 mm/y in the study area. The results shown in this paper demonstrate the reasonable potential of GF-3 SAR images to monitor land subsidence.

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Section: Data and The Study Areamentioning

confidence: 99%

Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms

Wang

Deng

et al. 2019

Journal of Sensors

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“…It was launched in August 2016. It has multiple strip and scan imaging modes [32]. In this paper, we annotated 20 narrow-scan (NSC) mode images manually with the help of the corresponding AIS information and expert inspections.…”

Section: Data Set Descriptionmentioning

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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“…Gaofen-3 (GF-3) satellite is China's first civil C-band high-resolution quad-pol SAR satellite specifically missioned for ocean remote sensing. The nominal highest resolution of GF-3 data is up to 1 m. GF-3 data have been widely used on applications such as ship recognition [6][7][8][9][10], aircraft detection [11], and image translation between optical and SAR images [12].…”

Section: Introductionmentioning

confidence: 99%

FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

Hou

Song

et al. 2020

Sci. China Inf. Sci.

157

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Gaofen-3 (GF-3) is China's first civil C-band fully polarimetric spaceborne synthetic aperture radar (SAR) primarily missioned for ocean remote sensing and marine monitoring. This paper proposes an automatic sea segmentation, ship detection, and SAR-AIS matchup procedure and an extensible marine target taxonomy of 15 primary ship categories, 98 sub-categories, and many non-ship targets. The FUSAR-Ship high-resolution GF-3 SAR dataset is constructed by running the procedure on a total of 126 GF-3 scenes covering a large variety of sea, land, coast, river and island scenarios. It includes more than 5000 ship chips with AIS messages as well as samples of strong scatterer, bridge, coastal land, islands, sea and land clutter. FUSAR-Ship is intended as an open benchmark dataset for ship and marine target detection and recognition. A preliminary 8-type ship classification experiment based on convolutional neural networks demonstrated that an average of 79% test accuracy can be achieved.

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Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Cited by 28 publications

References 40 publications

Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms

Demonstration of Time-Series InSAR Processing in Beijing Using a Small Stack of Gaofen-3 Differential Interferograms

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

FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

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