BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images

Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

Oil Spill Detection Using Fluorometric Sensors: Laboratory Validation and Implementation to a FerryBox and a Moored SmartBuoy

A large part of oil spills happen near busy marine fairways. Presently, oil spill detection and monitoring are mostly done with satellite remote sensing algorithms, or with remote sensors or visual surveillance from aerial vehicles or ships. These techniques have their drawbacks and limitations. We evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills. The sensors were capable of detecting diesel oil for at least 20 days in laboratory conditions, but the presence of CDOM, turbidity and algae-derived substances substantially affected the detection capabilities. Algae extract was observed to have the strongest effect on the fluorescence signal, enhancing the signal in all combinations of sensors and solutions. The sensors were then integrated to a FerryBox system and a moored SmartBuoy. The field tests support the results of the laboratory experiments, namely that the primary source of the measured variation was the presence of interference compounds. The 2 month experiments data did not reveal peaks indicative of oil spills. Both autonomous systems worked well, providing real-time data. The main uncertainty is how the sensors' calibration and specificity to oil, and the measurement depth, affects oil detection. We recommend exploring mathematical approaches and more advanced sensors to correct for natural interferences.

Oil Spill Detection Using Satellite Imagery

<span>Since oil exploration began, oil spills have become a serious problem. When drilling for oil, there is always a risk of an oil spill. With the new development of technology over the years, oil spill detection has become much easier making the clean-up of a spill to happen much faster reducing the risk of a large spread. In this study, remote sensing techniques were used to detect the Deep-water Horizon oil spill through a change detection method. The change detection method allows the viewer to determine the difference of an area before and after an oil spill as well as detect the irregular difference on a surface. To confirm the effectiveness of change detection method, two approaches were used each showing the differences in the images before and after the spill allowing the size and shape to be identified. The swipe tool in the ArcGIS software was used to visually show the changes. The difference tool was also used to both visually and statistically to investigate the difference before and after the Deep-water Horizon oil spill event.</span>

Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions

In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case. In particular, we propose a novel online nonparametric Bayesian analysis method based on the infinite Gamma mixture model where the determination of the number of clusters is bypassed via an infinite number of mixture components. The proposed model is learned via an online extended variational Bayesian inference approach in a flexible way where the priors of model’s parameters are selected appropriately and the posteriors are approximated effectively in a closed form. The online setting has the advantage to allow data instances to be treated in a sequential manner, which is more attractive than batch learning especially when dealing with massive and streaming data. We demonstrated the performance and merits of the proposed statistical framework with a challenging real-world application namely oil spill detection in synthetic aperture radar (SAR) images.