Ship Detection by the RADARSAT SAR: Validation of Detection Model Predictions

Vachon, P.W.; Campbell, James W.; Bjerkelund, C; Dobson, Fred W.; Rey, María Teresa de Tena

doi:10.1080/07038992.1997.10874677

Cited by 157 publications

(19 citation statements)

References 15 publications

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“…In the following these parameters will be called influencing parameters. While different approaches for modeling ship detectability have been published in past years and tuned for various SAR missions [10][11][12][13], a model for the detectability of wake signatures using real data has only been published recently [14]. However, theoretical assumptions about the dependency of influencing parameters with the detectability of certain components of wakes using simulated data and physical contemplations exist since decades [15].…”

Section: Introductionmentioning

confidence: 99%

Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar

et al. 2019

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The physics of the imaging mechanism underlying the emergence of ship wakes in Synthetic Aperture Radar (SAR) images has been studied in the past by many researchers providing a well-understood theory. Therefore, many publications describe how well ship wakes are detectable on SAR under the influence of different environmental conditions like sea state or local wind, ship properties like ship speed or ship heading, and image acquisition parameters like incidence angle or satellite heading. The increased imaging capabilities of current satellite SAR missions facilitate the collection of large datasets of moving vessels. Such a large dataset of high resolution TerraSAR-X acquisitions now enables the quantitative analysis of the previously formulated theory about the detectability of ship wakes using real data. In this paper we propose an extension of our wake detectability model by using a non-linear basis which allows consideration of all the influencing parameters simultaneously. Such an approach provides new insights and a better understanding of the non-linear influence of parameters on the wake detectability and their interdependencies can now be represented. The results show that the non-linear, interdependent influence of the different influencing parameters on the detectability of wakes matches well to the oceanographic expectations published in the past. Also possible applications of the model for the extraction of missing parameters and automatic for wake detection systems are demonstrated.

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Section: Introductionmentioning

confidence: 99%

Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar

et al. 2019

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“…In terms of AIS data, which are widely used to validate ship detection, the best choice is that the acquisition of SAR images and AIS data is synchronous. In most cases, AIS-SAR latency within several minutes is permissible [58][59][60][61]. The time difference allowed between the two observations depends on the ship density [61].…”

Section: Validationmentioning

confidence: 99%

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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“…In order to provide a high quality Near Real Time (NRT) ship detection service, expected performances in terms of probability of detection and minimum ship size are needed. Previous studies, see [5]- [9], have approached the topic by building a prediction model which takes into account the underlying statistic of the ocean clutter radar backscatter distribution and relating the ship size to the maximum Radar Cross Section (RCS) of the ships. In this paper, a detectability model based on support vector classification is indeed proposed for both TerraSAR-X and Sentinel-1.…”

Section: Motivationmentioning

confidence: 99%

Comparison of ship detectability between TerraSAR-X and Sentinel-1

Velotto

Tings

Bentes

2017

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)

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In this paper, the detectability of ship signatures in Synthetic Aperture Radar (SAR) imagery acquired by the TerraSAR-X/TanDEM-X and Sentinel-1 is compared. The comparison takes into account different sensors acquisition parameters and environmental conditions on a large variety of ship size and types. In the first step, ocean targets are detected using the Near Real Time (NRT)-optimized Constant-False-Alarm-Rate (CFAR) algorithm. The optimizations include the ocean/land and false targets discrimination. In the second step, all detected targets are automatically matched in space and time with the recorded Automatic Identification System (AIS) messages. A manual cross-check is performed at the end of the assignments to have a clean SAR ship signature database. Additionally, the local wind field is retrieved from the SAR backscatter of the ocean surface surrounding the detected ships, by applying the Geophysical Model Functions (GMF) inversion XMOD2 for X-band data and CMOD5 for C-band data. Similarly, the local sea state conditions are calculated by the XWAVE and CWAVE empirical model functions. The final detectability model takes into account all SAR-based information, i.e. wind speed and sea state, as well as relevant SAR parameters, e.g. incidence angle. The overall probability of detection are derived for three ship size categories, i.e. small, medium and large, adopting an L2regularized Logistic Regression classifier trained on detected and nondetected ship samples.

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Ship Detection by the RADARSAT SAR: Validation of Detection Model Predictions

Cited by 157 publications

References 15 publications

Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar

Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar

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

Comparison of ship detectability between TerraSAR-X and Sentinel-1

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