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

Abstract

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.

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

“…Developing an accurate transferable approach for large-scale mapping of date palm trees from UAV images can be challenging because feature values may vary significantly based on the source of data, image object segmentation level, and intraclass variability among classes, given the dependence of traditional machine learning techniques on the selection of shallow handcrafted features (i.e., band ratio, color invariants, and geometrical features). Thus, misclassification is expected when traditional machine learning is applied to different imageries [108].…”

“…The mapping and detection of individual tree crowns, tree/plant/vegetation species, crops, and wetlands from UAV-based images are achieved by diverse CNN architectures, which are used to perform different tasks, including path-based classification [78][79][80][81][82][83][84][85][86][87], object detection [88][89][90][91][92][93][94][95][96][97], and semantic segmentation [98][99][100][101][102][103][104][105][106][107]. Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108]. Various architectures, such as fully convolutional networks (FCNs), SegNet [109], U-Net [110], and DeepLab V3+ [111], have been used successfully to delineate tree and vegetation species [70,98,100,101,103,105,106,[112][113][114]…”

Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

“…Some researchers have focused on obtaining information about automatic [18] or semiautomatic approaches [19], while others rely on human interpretation [20] to identify oil in SAR imagery. Most of these discrimination algorithms involve complex machine learning techniques, e.g., the Mahalanobis classifier [21], artificial neural networks [22], fuzzy logic [23], decision trees [24], among others; Al-Ruzouq et al [25] reviews the most frequently used machine learning techniques used for oil slick detection. These methods also use many complicated attributes; Espedal and Johannessen [26] and Stathakis et al [27] provide an extensive compilation of frequently used attributes.…”

Remote Sensing of Dispersed Oil Pollution in the Ocean—The Role of Chlorophyll Concentration

“…As a result, it reduces biodiversity and consequently accelerates the eutrophication process [ 18 , 19 ]. Despite the widely researched spilled oil including in situ, ship-borne, airplane and space-borne techniques [ 20 , 21 ], the fates of dispersed oil in seawater are much less known and are not monitored on a regular basis. Recently dispersed oil gained attention in marine research; however, most studies focus on its biological and ecological impact [ 22 , 23 ], chemical and microbiological consequences [ 24 , 25 , 26 ] or on oil spill modeling [ 27 , 28 , 29 ].…”