Processing framework to support maritime surveillance applications based on optical remote sensing images

Voinov, Sergey; Schwarz, Egbert; Krause, Detmar; Berg, Matthias

doi:10.1117/12.2326058

Cited by 4 publications

(4 citation statements)

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“…Imagery data also appear as a non-negligible input with associated remote sensing techniques (e.g. [76,77]). Radar and imagery data-based studies often do not focus on a specific activity, but on objects moving at the sea-surface, be it ships, boats, vessels or mobiles.…”

Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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Maritime Spatial Planning (MSP) has become of key interest in many countries around the world in the last decade, leading to an increased need to inform the historical spatial and temporal footprint of maritime activities. If this knowledge was still recently considered a blind spot, recent developments of maritime surveillance systems (e.g. AIS, VTS, VMS) have allowed to fill some of these gaps by generating significant amounts of spatial data on ships at sea. In this paper, the use of these maritime surveillance data for planning purposes is explored through the lens of the international scientific literature. A first set of 2030 articles dealing with maritime surveillance data and collected through the Web of Science collection was explored to determine the type of data used, the maritime activities addressed and the main objective of each paper (technical developments, safety and security, environmental protection, MSP). This allowed to highlight the growing interest on maritime surveillance data in the scientific literature over the past decades, predominantly towards AIS data and shipping. Over the 2030 papers, only 63 dealt specifically with MSP. These were extracted and explored, allowing to highlight the potential of these data to feed in MSP processes, with a specific focus on fisheries. Nevertheless, the description of actual uses of surveillance-based information within MSP processes remained particularly rare. If this can be seen as a result of a science-policy delay, we suggest that it is mostly related to issues of accessibility, acceptance by economic stakeholders and appropriation by decision makers.

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Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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“…Sentinel imagery can be accessed through ESA portals 2 , directly or via API access. More intuitive access is available through 3rd party providers such as Sinergise Sentinel Hub 3 , that is also integrated into open-source Earth observation library eo-learn 4 . This library was used to retrieve imagery in this paper, along with additional capabilities such as cloud detection.…”

Section: A Esa Sentinelmentioning

confidence: 99%

“…Ship detection from optical imagery has become an active research area in recent years, especially with the advent of deep-learning based methods, which have greatly increased the performance and also led to first operational systems [4]. Majority of research, as well as developed operational systems utilizing ship detection based on optical imagery, are focused on very-high-resolution (VHR) imagery, with spatial resolution up to 30cm.…”

Section: Introductionmentioning

confidence: 99%

Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery

Stepec,

Martincic,

Skocaj

2021

Preprint

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In this paper we present a ship detection pipeline for low-cost medium resolution satellite optical imagery obtained from ESA Sentinel-2 and Planet Labs Dove constellations. This optical satellite imagery is readily available for any place on Earth and underutilized in maritime domain, compared to existing solutions based on synthetic-aperture radar (SAR) imagery. We developed ship detection method based on state-of-the-art deeplearning based object detection method which was developed and evaluated on a large scale dataset that was collected and automatically annotated with the help of Automatic Identification System (AIS) data.

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“…For example, the Copernicus Sentinel-1(A/B) satellites may cover up to 250 km wide area in Interferometric (IW) swatch mode with 5x20 meters of ground sampling distance. Medium resolution (MR) multispectral optical sensors like Copernicus Santinel-2 or Landsat-8 are beneficial for environmental monitoring tasks due to their spectral resolution (Voinov, et al, 2018). Moreover MR sensors are capable to cover relatively large areas, sometimes comparable to SAR missions.…”

Section: Introductionmentioning

confidence: 99%

GeoForum MV 2020 – Geoinformation als Treibstoff der Zukunft

Bill¹,

Zehner²

2020

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Processing framework to support maritime surveillance applications based on optical remote sensing images

Cited by 4 publications

References 1 publication

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery

GeoForum MV 2020 – Geoinformation als Treibstoff der Zukunft

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