Canadian Progress Toward Marine and Coastal Applications of Synthetic Aperture Radar

Vachon, P.W.; Adlakha, Paul; Edel, H.; Henschel, Michael D.; Ramsay, B.; Flett, D.; Rey, María Teresa de Tena; Staples, Gordon; Thomas, Scott L.

doi:10.4095/219553

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…Besides, more than 10 additional SAR sensors will be launched over the next 10 years [ 8 ]. At the same time, several organizations have developed a number of SAR ship detection systems, such as OMW (Ocean Monitoring Workstation) [ 9 ], IMPAST (Improving Fisheries Monitoring Through Integrating Passive and Active Satellite Based Technologies) [ 10 ], SIMONS (Ship Monitoring System) [ 11 ] and ShipSurveillance [ 12 ], etc. However, most of them are designed for early SAR sensors, such as ERS (European Remote Sensing satellites) and ENVISAT (ENVIronmental SATellite).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Leng

Zhou

et al. 2016

Sensors

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With the rapid development of spaceborne synthetic aperture radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Leng

Zhou

et al. 2016

Sensors

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“…Constant False‐Alarm Rate (CFAR) algorithm is a mature method in marine target detection at present (Xing et al., 2012). It is the most common and effective detection algorithm in the field of Radar Signal Detection, including two‐parameter CFAR algorithm (Wackerman et al., 2001), adaptive two‐parameter CFAR detection method (Joshi et al., 2019), CFAR algorithm based on K‐distribution (Vachon, Adlakha, et al., 2000; Vachon, Thomas, et al., 2000), CFAR algorithm based on Weibull distribution etc. (Jiang et al., 2000).…”

Section: Introductionmentioning

confidence: 99%

Marine Target Extraction Based on Adjoint Covariance Correction Model

Liu

Zhang

et al. 2022

Radio Science

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Constant False‐Alarm Rate (CFAR) algorithm is a mature method in marine target detection at present, the key of which is the choice of probability model. The most classical probability model is Gaussian distribution, which is suitable for modeling the calm ocean surface. Complex ocean conditions generally do not obey to Gaussian distribution, which limits the performance of CFAR algorithm. Therefore, we propose Adjoint Covariance Correction Model (ACCM) to improve the performance of CFAR marine target detection. Analyzing the distribution characteristic of ocean clutter under complex ocean conditions based on experiments, we find that it has long‐tail characteristic. Aiming at this characteristic, we have conducted fitting experiment on 23 kinds of probability density functions (PDFs) and find Loglogistic distribution has a better fitting effect on long‐tail characteristic, therefore, we use it to model ocean clutter under complex ocean conditions for the first time. In order to further improve the fitting goodness, we add a variance correction term to the Loglogistic model to construct ACCM for marine target extraction. The experiment result shows that ACCM effectively fits the long‐tail characteristic caused by Synthetic Aperture Radar backscatter under complex ocean conditions. The goodness of fit improves by 50% compared with Loglogistic model, and the amount of false alarms is 81.42% of that of Loglogistic model. The extraction accuracy of marine target characteristic parameter based on ACCM is 11.58% higher than that of Loglogistic model, and 12.18% higher than that of two‐parameter CFAR model based on standard error function (named SEF‐CFAR).

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“…After processing, Nocak et al [6] used a global thresholding algorithm to complete ship detection. In the work of Vachon et al [7], K distribution was used as a marine clutter distribution model. To build a fractal model of ships, Kaplan [8] introduced Hurst parameters and proposed extended fractal features for identifying targets of specific sizes in RS images.…”

Section: Introductionmentioning

confidence: 99%

Priority Branches for Ship Detection in Optical Remote Sensing Images

et al. 2020

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Much attention is being paid to using high-performance convolutional neural networks (CNNs) in the area of ship detection in optical remoting sensing (ORS) images. However, the problem of false negatives (FNs) caused by side-by-side ships cannot be solved, and the number of false positives (FPs) remains high. This paper uses a DLA-34 network with deformable convolution layers as the backbone. The network has two priority branches: a recall-priority branch for reducing the number of FNs, and a precision-priority branch for reducing the number of FPs. In our single-shot detection method, the recall-priority branch is based on an anchor-free module without non-maximum suppression (NMS), while the precision-priority branch utilizes an anchor-based module with NMS. We perform recall-priority branch functions based on the output part of the CenterNet object detector to precisely predict center points of bounding boxes. The Bidirectional Feature Pyramid Network (BiFPN), combined with the inference part of YOLOv3, is used to improve the precision of precision-priority branch. Finally, the boxes from two branches merge, and we propose priority-based selection (PBS) for choosing the accurate ones. Results show that our proposed method sharply improves the recall rate of side-by-side ships and significantly reduces the number of false alarms. Our method also achieves the best trade-off on our improved version of HRSC2016 dataset, with 95.57% AP at 56 frames per second on an Nvidia RTX-2080 Ti GPU. Compared with the HRSC2016 dataset, not only are our annotations more accurate, but our dataset also contains more images and samples. Our evaluation metrics also included tests on small ships and incomplete forms of ships.

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Canadian Progress Toward Marine and Coastal Applications of Synthetic Aperture Radar

Cited by 12 publications

References 4 publications

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Marine Target Extraction Based on Adjoint Covariance Correction Model

Priority Branches for Ship Detection in Optical Remote Sensing Images

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