Remote Sensing of Ocean Oil-Spill Pollution

Solberg, Anne

doi:10.1109/jproc.2012.2196250

Cited by 176 publications

(110 citation statements)

References 51 publications

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“…Finally, the image data are published as WMS service for map displaying and retrieval by web-based map client like OpenLayers 13 or Ka-Map 14 ). 11 MapServer, http://www.mapserver.org 12 TileCache, http://tilecache.org 13 OpenLayers, http://en.wikipedia.org/wiki/OpenLayers 14 Ka-Map, http://ka-map.maptools.org/index.phtml?page=home.html…”

Section: Rs Data Subscription and Distributionmentioning

confidence: 99%

“…Particularly, many timecritical RS applications even demand real-time or near realtime processing capacities ( [7][8]). Some relevant examples are large debris flow investigation ( [9], flood hazard management ( [10]) and large ocean oil spills surveillance ( [11] [12]). Generally, these large-scale data processing problems in RS applications ([4][13] [14]) with high QoS requirement are typically regarded as both compute-intensive and data-intensive.…”

Section: Introductionmentioning

confidence: 99%

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pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

169

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With the increasing requirement of accurate and up-to-date resource & environmental information for regional and global monitoring, large-region covered multi-temporal, multi-spectral massive remote sensing (RS) datasets are exploited for processing. The remote sensing data processing generally follows a complex multi-stage processing chain, which consists of several independent processing steps subject to types of RS applications. In general the RS data processing for regional environmental and disaster monitoring are recognized as typical both compute-intensive and data-intensive applications.To solve the aforementioned issues efficiently, we propose pipsCloud which combine recent Cloud computing and HPC techniques to enable large-scale RS data processing system as on-demand real-time services. Benefiting from the ubiquity, elasticity and high-level of transparency of Cloud computing model, the massive RS data managing and data processing for dynamic environmental monitoring are all encapsulate as Cloud with Web interfaces. Where, a Hilbert-R + based data indexing mechanism is employed for optimal query and access of RS imageries, RS data products as well as interim data. In the core platform beneath the Cloud services, we provide a parallel file system for massive high-dimensional RS data and offers interfaces for intensive irregular RS data accessing so as to provide improved data locality and optimized I/O performance. Moreover, we adopt an adaptive RS data analysis workflow manage system for on-demand workflow construction and collaborative execution of distributed complex chain of RS data processing, such as forest fire detection, mineral resources and coastline monitoring. Through the experimental analysis we have show the efficiency of the pipsCloud platform.

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Section: Rs Data Subscription and Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

169

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“…This might be a result of the higher oil fraction in E80 compared to E60 and E40. The viscosities of the emulsion slicks are characterized by higher viscosity than the natural film [66]. Hence, the emulsion slicks should have a stronger damping of the ocean waves and thus be more detectable than the plant oil.…”

Section: Separability As a Function Of Timementioning

confidence: 99%

Analysis of Evolving Oil Spills in Full-Polarimetric and Hybrid-Polarity SAR

Espeseth

Skrunes

Jones

et al. 2017

IEEE Trans. Geosci. Remote Sensing

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Abstract-Oil spill detection using a time series of images acquired off Norway in June 2015 with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is examined. The relative performance of a set of features derived from quadpolarization vs. hybrid-polarity modes in detection of various types of slicks as they evolve on a high wind driven sea surface is evaluated. It is shown that the hybrid-polarity mode is comparable to the full-polarimetric mode in its ability to distinguish the various slicks from open water for challenging conditions of high winds (9-12 m/s), small release volumes (0.2 -0.5m3 ), and during the period 0-9 hours following release. The features that contain the cross-polarization component are better for distinguishing the various slicks from open water at later and more developed stages. Although these features are not available in the hybrid-polarity mode, we identify alternative features to achieve similar results. In addition, a clear correlation between the results of individual features and their dependence on particular components within the two-scale Bragg scattering theory is identified. The features that show poor detectability of the oil slicks are those that are independent of the small-scale roughness, while the features resulting in good separability were dependent on several factors in the two-scale Bragg scattering model. We conclude that the hybrid-polarity mode is a viable alternative for SAR-based oil spill detection and monitoring that provides comparable results to those from quad-polarimetric SAR.

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“…Detection of oil spills is a long-term objective in sea remote sensing [1], [2]. The oil contaminated sea water is a threat to the marine environment.…”

Section: Introductionmentioning

confidence: 99%