Monitoring the Coastal Zone with the RADARSAT Satellite

Vachon, P.W.; Thomas, Scott L.; Cranton, J.; Bjerkelund, C; Dobson, Fred W.; Olsen, R.C.

doi:10.4095/219330

Cited by 5 publications

(3 citation statements)

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“…Overtime, several methods have been adopted for the dark spot detection. Manual cropping of dark spot formation and the use of threshold-based techniques were applied in early segmentation studies wherein bimodal N * N pixels (in which N = 25 pixels) histogram was used [123][124][125]. Vyas et al [126] adopted a two-step methodological approach for spot detection and extraction using global threshold for pre-processing and pixel sampling.…”

Section: Dark Spot Detectionmentioning

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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Section: Dark Spot Detectionmentioning

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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“…The land areas were masked using the ASTER GDEM data. In addition, ship features having strong backscattering were detected from VH-polarized NRCSs by applying adaptive thresholding method [74]- [75] and then masked out. The remaining ocean pixels were averaged in a 20 × 20 moving window to reduce speckle noise.…”

Section: A Derivation Of First-guess Wind Speedmentioning

confidence: 99%

Observation of Wind Direction Change on the Sea Surface Temperature Front Using High-Resolution Full Polarimetric SAR Data

Kim

Park

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this study, we derive high-resolution wind speeds and directions from full-polarization synthetic aperture radar (SAR) data. Previous wind retrieval result from conventional single-polarization SAR data has a limitation to resolve small-scale structures in the surface wind because external wind direction data with coarser spatial resolution than those of SARs have been commonly used as an input. Using fully polarimetric SAR data, however, both wind speed and direction can be derived with high resolution from the image itself without any ancillary data. We derive wind field off the southern coast of Korea from the Radarsat-2 quad-polarization data and investigate the spatial variation. The retrieved wind field from the Radarsat-2 image presents a detailed structure including small-scale variations which is unobtainable from conventional wind observations. Comparison of the derived wind directions with insitu buoy wind measurements shows a small difference of 8° which is regarded as sufficient to analyze small-scale wind vector changes. The retrieved wind field off the southern coast of Korea demonstrates the distinct patterns of direction changes. While blowing over the sea surface temperature (SST) frontal zone, the veering angles of wind vectors decrease and then are restored. The analysis of SAR-derived wind vectors with coinciding temperature distributions confirms that the variation in SAR-derived wind vectors on the SST fronts is mainly induced by the stability effect. This study also addresses the important role of precise wind direction retrieval on the accuracy of retrieved wind speed.

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“…A first approach is described by Skøelv and Wahl (1993) for ERS-1 SAR images). A similar approach is described in Vachon et al (1998) and Manore et al (1998). Solberg et al (1999Solberg et al ( , 2003 apply an adaptive algorithm where the threshold is dynamically set at k dB below the mean value estimated in a moving window.…”

Section: Other Techniquesmentioning

confidence: 99%