Automatic decision support system based on SAR data for oil spill detection

This is the accepted manuscript of the following article: Mera, D., Cotos, J., Varela-Pet, J., G. Rodríguez, P. and Caro, A. (2014). Automatic decision support system based on SAR data for oil spill detection. Computers & Geosciences, 72, pp.184-191Global trade is mainly supported by maritime transport, which generates important pollution problems. Thus, effective surveillance and intervention means are necessary to ensure proper response to environmental emergencies. Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillages on the oceans surface. Several Decision Support Systems have been based on this technology. This paper presents an automatic oil spill detection system based on SAR data which was developed on the basis of confirmed spillages and it was adapted to an important international shipping route off the Galician coast (northwest Iberian Peninsula). The system was supported by an adaptive segmentation process based on wind data as well as a shape oriented characterization algorithm. Moreover, two classifiers were developed and compared. Thus, image testing revealed up to 95.1% candidate labeling accuracy. Shared-memory parallel programming techniques were used to develop algorithms in order to improve above a 25% of the system processing timeThe authors wish to thank the financial support provided by the ‘Deputación da Coruña’ under the ‘Bolsas de Investigación 2013’ programmeS

Repair Wind Field of Oil Spill Regional Using SAR Data

In this paper, we compared the normalized radar cross section (NRCS) of the synthetic aperture radar in the cases of oil spill and clean sea areas with image samples and determined their thresholds of the NRCS of SAR. we used the NRCS of clean water from the adjacent patches spill area to replace NRCS of oil spill area and retrieval wind field by CMOD5.N and comparison of wind velocity mending of oil spill with Model data the root mean square of wind speed and wind direction inversion are 0.89m/s and 20.26 satisfactory results, respectively. Therefore, after the occurrence not large scale oil spill, the real wind field could be restored by this method.&nbsp

Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images

Oil spillage over a sea or ocean surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data have been used efficiently for the detection of oil spills due to their operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, lookalikes, ships, and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification and introduce a new loss function, namely, “gradient profile” (GP) loss, which is in fact the constituent of the more generic spatial profile loss proposed for image translation problems. For the purpose of training, testing, and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard, and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, lookalikes, ships, and land class is 96.00%, 60.87%, 74.61%, and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different convolutional neural networks (CNNs) and vision transformers (ViTs)-based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F$_1$ scores for CNNs as well as ViTs-based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

2015 Oil Observing Tools: A Workshop Report

Since 2010, the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have provided satellite-based pollution surveillance in United States waters to regulatory agencies such as the United States Coast Guard (USCG). These technologies provide agencies with useful information regarding possible oil discharges. Unfortunately, there has been confusion as to how to interpret the images collected by these satellites and other aerial platforms, which can generate misunderstandings during spill events. Remote sensor packages on aircraft and satellites have advantages and disadvantages vis-à-vis human observers, because they do not “see” features or surface oil the same way. In order to improve observation capabilities during oil spills, applicable technologies must be identified, and then evaluated with respect to their advantages and disadvantages for the incident. In addition, differences between sensors (e.g., visual, IR, multispectral sensors, radar) and platform packages (e.g., manned/unmanned aircraft, satellites) must be understood so that reasonable approaches can be made if applicable and then any data must be correctly interpreted for decision support. NOAA convened an Oil Observing Tools Workshop to focus on the above actions and identify training gaps for oil spill observers and remote sensing interpretation to improve future oil surveillance, observation, and mapping during spills. The Coastal Response Research Center (CRRC) assisted NOAA’s Office of Response and Restoration (ORR) with this effort. The workshop was held on October 20-22, 2015 at NOAA’s Gulf of Mexico Disaster Response Center in Mobile, AL. The expected outcome of the workshop was an improved understanding, and greater use of technology to map and assess oil slicks during actual spill events. Specific workshop objectives included: •Identify new developments in oil observing technologies useful for real-time (or near real-time) mapping of spilled oil during emergency events. •Identify merits and limitations of current technologies and their usefulness to emergency response mapping of oil and reliable prediction of oil surface transport and trajectory forecasts.Current technologies include: the traditional human aerial observer, unmanned aircraft surveillance systems, aircraft with specialized senor packages, and satellite earth observing systems. •Assess training needs for visual observation (human observers with cameras) and sensor technologies (including satellites) to build skills and enhance proper interpretation for decision support during actual events

Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches