Intelligent hybrid system for dark spot detection using SAR data

Genovez, Patrcia; Ebecken, Nelson F. F.; Freitas, Corina da Costa; Bentz, Cristina Maria; Freitas, Ramon Morais de

doi:10.1016/j.eswa.2017.03.037

Cited by 18 publications

(15 citation statements)

References 40 publications

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“…There are many spill look-alikes which interfere with radar imagery. Many studies have been devoted to removing the look-alikes from radar imagery [ 44 ]. Further, there are other aberrations to oil spill mapping by radar.…”

Section: Use Of Active Sensors For Oil Spill Detection and Mappingmentioning

confidence: 99%

A Review of Oil Spill Remote Sensing

Fingas¹,

Brown

2017

Sensors

279

171

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The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day–night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.

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Section: Use Of Active Sensors For Oil Spill Detection and Mappingmentioning

confidence: 99%

A Review of Oil Spill Remote Sensing

Fingas¹,

Brown

2017

Sensors

279

171

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“…[33][34][35] Microwave sensors used microwave radar technology, which can collect oil spillage data at any time of the day and under any weather conditions, independent of cloud coverage, providing a wider range of uses. 36,37 However, the biggest challenge of using synthetic aperture radar images from remote microwave sensing technologies comes from interference with a variety of man-made or natural objects such as plankton, sh, algae, wave, and air turbulence. 34,36,[38][39][40] Most of these on-line remote sensing techniques using very expensive devices had added a lot of cost to the oil spillage monitoring work.…”

Section: Introductionmentioning

confidence: 99%

On-site marine oil spillage monitoring probes formed by fixing oxygen sensors into hydrophobic/oleophilic porous materials for early-stage spotty pollution warning

Shi

Jiang

et al. 2021

RSC Adv.

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“…Both sensors have been applied to oil spill detection, but microwave sensors enjoy broader usage due to the ability to capture data at any time of the day and under any weather condition without being affected by cloud cover [25,30,36,37]. This explains the widespread adoption of Radar microwave technology for oil spill detection [28,[38][39][40][41]. In identifying oil spills in water bodies, the satellites emit microwave and detect the reflected wave in return.…”

Section: Introductionmentioning

confidence: 99%

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Yekeen

Balogun

2020

Remote Sensing

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Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

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Intelligent hybrid system for dark spot detection using SAR data

Cited by 18 publications

References 40 publications

A Review of Oil Spill Remote Sensing

A Review of Oil Spill Remote Sensing

On-site marine oil spillage monitoring probes formed by fixing oxygen sensors into hydrophobic/oleophilic porous materials for early-stage spotty pollution warning

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

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