This paper presents a comparative study comparing four operational detectors that work by automatically postprocessing Synthetic Aperture Radar (SAR) images acquired from the satellite platforms RADARSAT-2 and COSMO-SkyMed. Challenging maritime scenarios have been chosen to assess the detectors' performance against features such as ambiguities, significant sea clutter or irregular shorelines. The SAR images which form the test data are complemented with ground-truth to define the reference detection configuration, which permits quantifying the Probability of Detection (PoD), the False Alarm Rate (FAR) and the accuracy of estimating ship dimensions. Although the results show that all the detectors perform notably well, there is no perfect detector, and in future work a better detector could be developed that combines the best elements from each of the detectors. Beyond the pure comparison exercise, the study has permitted improving the detectors by pointing weaknesses out and providing means for fixing them.
In this article, a novel technique for fully automatic vessel size estimation using medium-to-high-resolution synthetic aperture radar (SAR) images is presented. Based on mathematical morphology, it aims at better delineating the vessel outline in the cluttered SAR image, thereby enabling the extraction of its actual dimensions. The technique has been tested on a set of 127 ships representing a range in lengths between 24 and 366 m in five Sentinel-1 images at 20 m multilook resolution that have good quality ground truth available. It is found that the proposed algorithm produces very good length estimates (15% relative error/30 m absolute error) and reasonable width estimates (35%/11 m). The estimates are significantly better than those from a simpler automatic method that does not use mathematical morphology, and approach those from manual analysis by an expert.
ARTICLE HISTORY
The presence of border noise in Sentinel-1 Ground Range Detected (GRD) products is an undesired processing artifact that limits their full exploitation in a number of applications. All of the Sentinel-1 GRD products generated before March 2018—more than 800,000—are affected by this particular type of noise. In March 2018, an official fix was deployed that solved the problem for a large portion of the newly generated products, but it did not cover the entire range of products, hence the need for an operational tool that is able to effectively and consistently remove border noise in an automated way. Currently, a few solutions have been proposed that try to address the problem, but all of them have limitations. The scope of this paper is therefore to present a new method based on mathematical morphology for the automatic detection and masking of border noise in Sentinel-1 GRD products that is able to overcome the existing limitations. To evaluate the performance of the method, a detailed numerical assessment was carried out, using, as a benchmark, the ‘Remove GRD Border Noise’ module integrated in ESA’s Sentinel Application Platform. The results showed that the proposed method is capable of very accurately removing the undesired noisy pixels from GRD images, regardless of their acquisition mode, polarization, or resolution and can cope with challenging features within the image scenes that typically affect other approaches.
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