Oil Spill Four-Class Classification Using UAVSAR Polarimetric Data

Hassani, Behnam; Sahebi, Mahmod Reza; Asiyabi, Reza Mohammadi

doi:10.1007/s12601-020-0023-9

Cited by 10 publications

(8 citation statements)

References 41 publications

(66 reference statements)

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“…OOS is a significant environmental problem that endangers the ocean ecosystems through multiple adverse effects [49]. OOS in open waters can be caused due to the ship colliding with obstacles, oil leakage from petroleum tankers, underwater oil pipelines, and technical defects in oil rigs [50].…”

Section: ) Ocean Oil Spill (Oos)mentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Amani¹,

Ghorbanian

Asgarimehr

et al. 2022

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

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Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, Remote Sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this paper, the goal is to review most remote sensing systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, Thermal Infrared (TIR) radiometers, microwave radiometers, and Global Navigation Satellite Systems (GNSS), are comprehensively discussed. Additionally, this paper summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.

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Section: ) Ocean Oil Spill (Oos)mentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Amani¹,

Ghorbanian

Asgarimehr

et al. 2022

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

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“…where SWIR refers to short wave infrared and Band SW IR refers to wavelengths in the following range: 2.09-2.35 µm. The NDVI and NBR values vary from −1 to 1, with 1 representing the greatest level of vegetative activity, and negatives values and those close to zero indicating little or no chlorophyll activity [24,44]. Burned frequency and severity are determined by differentiating between these two index layers:…”

Section: Validation Of the Spectral Indicesmentioning

confidence: 99%

Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Tariq

Hong

Gagnon

et al. 2021

Forests

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The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.

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“…However, existing review studies have yet to address the adoption of state-of-the-art machine learning (ML) techniques for identifying oil spills/slicks. The synergetic use of RS technologies and ML algorithms to detect and monitor oil spill slicks has been examined and adopted in a wide spectrum of studies [19][20][21][22][23][24][25][26][27]. ML techniques have demonstrated an effective means to extract oil spills from remotely sensed data in a (semi)automatic manner.…”

Section: Introductionmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Oil Spill Four-Class Classification Using UAVSAR Polarimetric Data

Cited by 10 publications

References 41 publications

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

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