Pol-SAR Based Oil Spillage Classification With Various Scenarios of Prior Knowledge

Liang, Xijun; Zhen, Zongjin; Song, Yong; Jian, Ling; Song, Dongmei

doi:10.1109/access.2019.2917953

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…We include articles like a paper on primary production appraisals as relevant for our review, as such an article not only communicate understanding of the dynamics of this relationship with oceanic food webs, energy process carbon cycle and earth's climate, but also provides an analysis ecological processes like fish resources [16]. We exclude scientific articles that do not have smart and sustainable fishery as its core, such as a paper on oil spillage classification in which scenario analysis is the core of the paper, rather than machine learning in fishery systems [17]. This selection criterion further narrowed down our result to 50.…”

Section: Methodsmentioning

confidence: 99%

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

2021

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Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable fishery. The purpose of this paper is to perform a systematic review of the literature on the smart fishery and to identify upcoming themes for future research on the sustainable fishery in the Age of AI. The findings of the review reveal that scholarly attention in AI-inspired fishery literature focuses mostly on automation of fishery resources monitoring, mainly detection, identification, and classification. Some papers list marine health and primary production which are vital dimensions for Large Marine Ecosystems to recycle nutrients to sustain anticipated production levels. Very few reviewed articles refer to assessing individual needs, particularly fishers, from AI deployment in fisheries and policy response from governments. We call for future AI for sustainable fishery studies on how fishers perceive AI needs, and how governments possess a tangible strategy or depth of understanding on the regulation of AI concerning smart fishery systems and research on resilience-enhancing policies to promote the value and potentials of the AI-inspired smart fishery in different locations.

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Section: Methodsmentioning

confidence: 99%

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

2021

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“…Sentinel-1 satellite data can be obtained from the European Space Agency (ESA) website (https://scihub.copernicus.eu, accessed on 11 March 2023). Sentinel-1 has a variety of imaging modes, working in single polarization and dual polarization modes [24,25]. Radar waves are susceptible to the physical traits and structures of the target, which ensures the good capability of imaging radar systems to obtain the scattering information of the target.…”

Section: Data Sourcesmentioning

confidence: 99%

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Liao,

Zhao,

Song

2023

Sustainability

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Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.

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“…Change detection algorithms (CDAs) are widely employed in different target detection through multitemporal analysis of SAR images [10]- [12]. Recently, artifcial intelligence techniques, particularly deep neural networks, have been employed as effective tools for target classifcation in different SAR image experiments too [13]- [15]. These methods were applied for concealed target detection in the CARABAS-II SAR images, the predecessor to the LORA system, resulting in a high probability of detection associated with a low false alarm rate [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign

Javadi,

Palm,

et al. 2023

2023 IEEE International Radar Conference (RADAR)

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Synthetic aperture radar (SAR) data are widely used for remote sensing applications, such as change detection and environmental monitoring. This paper presents a recent measurement campaign for SAR images using the LORA system and investigates the applicability of the collected data for change detection. The region of interest in this study is a busy commercial harbour area in the south of Sweden. During the measurements, there were signifcant changes on the ground in the parking lot as trucks were disembarking a ship. The obtained SAR images were frst fltered to have similar regions of interest in the Fourier domain to increase the coherence magnitude. Then, a constant false alarm rate (CFAR) algorithm was employed to detect changes with respect to trucks. In addition, optical aerial images were collected during this measurement campaign and were utilized to adjust the CFAR detection threshold. As a result, all the changed and unchanged regions corresponding to the selected targets were detected successfully. Moreover, a pattern of trucks' utilization of the harbour's parking lot during this peak time was obtained. The results demonstrate the applicability of the data from the ongoing measurement campaign and the possibility of further algorithm development for target detection and classifcation.

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Pol-SAR Based Oil Spillage Classification With Various Scenarios of Prior Knowledge

Cited by 9 publications

References 37 publications

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign

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