Ship Surveillance With TerraSAR-X

Brusch, Stephan; Lehner, Susanne; Fritz, Thomas; Soccorsi, Matteo; Soloviev, Alexander; Schie, B van

doi:10.1109/tgrs.2010.2071879

Cited by 393 publications

(228 citation statements)

References 9 publications

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“…Its mean µ water and standard deviation σ water are estimated from pixel values in a sliding window, which is hollow square shaped (Figure 2). Then, the threshold is calculated with the equation The CFAR detector has proven its usefulness already for ship detection (Scharf, 1991;Vachon, 1997;Brusch, 2011) and later has been applied to iceberg detection (Gill, 2001;Power, 2001). In (Buus-Hinkler, 2014), an adapted CFAR detector is utilized for detailed studies on iceberg frequency in Greenland waters.…”

Section: Iceberg Detectionmentioning

confidence: 99%

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Ressel

Frost

Lehner

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.

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Section: Iceberg Detectionmentioning

confidence: 99%

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Ressel

Frost

Lehner

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This is often applicable since the central limit theorem states that the average of a large number of identically distributed random variables tends to have a Gaussian distribution; obviously the more independent scatterers are in a resolution cell the more adequate the Gaussian model is. With this model it is possible to obtain a closed form for CFAR ship-detection algorithms with a computational saving compared to others methods adopting different distributions [5]. But the spatial resolution is tremendously improved in modern high resolution SARs and, consequently, the number of independent scatterers in the resolution cell is decreasing.…”

Section: Detectionmentioning

confidence: 99%

“…The threshold has to be chosen according to the image resolution: the higher the resolution, the larger the threshold value has to be set. In the second step, the azimuth ambiguities, due to the side lobes of the azimuth antenna pattern, are removed by evaluating the relative distance between the target and the replica as performed in [5] (ambiguities removal). It is possible to evaluate the azimuth displacement between the target and the first order ambiguities considering the equation (2).…”

Section: Discriminationmentioning

confidence: 99%

Novasar-S and maritime surveillance

Iervolino

Guida

Whittaker

2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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The paper shows a new algorithm for ship-detection from Synthetic Aperture Radar (SAR) images. The algorithm consists of three main stages: pre-processing, detection and discrimination. In the pre-processing a land mask is obtained considering the different statistics between the sea and the land's backscattered field; the detection stage isolates the bright points over the sea background employing a Constant False Alarm (CFAR) method; while the ships are retrieved, in the discrimination step, by evaluating the scattering contributions of the possible targets detected in the previous stage. The algorithm is tested on an airborne Sband SAR image of Portsmouth harbor, similar to those that will become available with the upcoming UK SAR mission NovaSAR-S.

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“…The provided results, though extensive and meaningful, were limited to RADARSAT, ASAR and ERS images. Only lately, Brusch et al (2011) have published a ship detection chain for TerraSAR-X images where they briefly showed the validation of ship length estimation from 'Stripmap MGD' very high-resolution (3.3 m) products.…”

Section: Introductionmentioning

confidence: 99%

The exploitation of Sentinel-1 images for vessel size estimation

Stasolla

Greidanus

2016

Remote Sensing Letters

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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

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Ship Surveillance With TerraSAR-X

Cited by 393 publications

References 9 publications

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Novasar-S and maritime surveillance

The exploitation of Sentinel-1 images for vessel size estimation

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