The exploitation of Sentinel-1 images for vessel size estimation

Stasolla, Mattia; Greidanus, H.

doi:10.1080/2150704x.2016.1226522

Cited by 26 publications

(20 citation statements)

References 16 publications

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“…Recently, plenty of experimental analyses have focused on the utilization of Sentinel-1 SAR data for maritime surveillance [14][15][16][17][18][19], strongly encouraged by free-accessed data. In Reference [18], a first comparison of Sentinel-1 and TerraSAR-X data is shown, and the wake appearance is investigated in only one case: a fishing vessel that shows a wake more pronounced in the VV-polarized C-band image than in the HH-polarized X-band image.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images

2017

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Abstract:A novel technique for ship wake detection has been recently proposed and applied on X-band Synthetic Aperture Radar images provided by COSMO/SkyMed and TerraSAR-X. The approach shows that the vast majority of wake features are correctly detected and validated in critical situations. In this paper, the algorithm was applied to 28 wakes imaged by Sentinel-1 mission with different polarizations and incidence angles with the aim of testing the method's robustness with reference to radar frequency and resolution. The detection process is properly modified. The results show that the features were correctly classified in 78.5% of cases, whereas false confirmations occur mainly on Kelvin cusps. Finally, the results were compared with the algorithm performance on X-band images, showing that no significant difference arises. In fact, the total false confirmations rate was 15.8% on X-band images and 18.5% on C-band images. Moreover, since the main criticality concerns again the false confirmation of Kelvin cusps, the same empirical criterion suggested for the X-band SAR images yielded a negligible 1.5% of false detection rate.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images

2017

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“…The problem of ship length estimation from SAR images was briefly discussed in [18,19]. In [18], the best shape of a ship was extracted from a SAR image using inertia tensors.…”

Section: Introductionmentioning

confidence: 99%

“…However, the absence of a ground truth does not allow validating the accuracy of this method. In [19], a three step method was proposed in order to extract a rectangle that would be the reference model for ship length estimation. The method produced good results (mean absolute error: 30 m ± 36.6 m).…”

Section: Introductionmentioning

confidence: 99%

Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

et al. 2019

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The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

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“…Recently, an improved method for ship size estimation was developed [72]; however, this is not yet implemented in SUMO.…”

Section: Estimation Of Size Heading and Rcsmentioning

confidence: 99%

“…Regarding the attributes of the detection, a quantitative evaluation of the accuracy of the ship size estimates by SUMO on Sentinel-1 images was published recently [72]. This evaluation used AIS data for reference and consequently did not include very small targets.…”

Section: Performance and Accuracymentioning

confidence: 99%

The SUMO Ship Detector Algorithm for Satellite Radar Images

et al. 2017

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Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C-and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1-100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU's Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

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The exploitation of Sentinel-1 images for vessel size estimation

Cited by 26 publications

References 16 publications

Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images

Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images

Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

The SUMO Ship Detector Algorithm for Satellite Radar Images

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