Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico

Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes

Studies of the Deepwater Horizon Oil Spill With the UAVSAR Radar

On 22- 23 June 2010, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L band radar imaged the Deepwater Horizon oil spill and the effects of oil that was transported within the Gulf of Mexico. We describe the campaign and discuss the unique contributions of the UAVSAR radar to the study of the detection, migration, and impact of oil from the spill. We present an overview of UAVSAR data analyses that support the original science goals of the campaign, namely, (1) algorithm development for oil slick detection and characterization, (2) mapping of oil intrusion into coastal wetlands and intercoastal waterways, and (3) ecosystem impact studies. Our study area focuses on oil-affected wetlands in Barataria Bay, Louisiana. The results indicate that fine resolution, low-noise, L band radar can detect surface oil-on-water with sufficient sensitivity to identify regions in a slick with different types of oil/emulsions and/or oil coverage; identify oil on waters in inland bays and differentiate mixed/weathered oil from fresh oil as it moves into the area; identify areas of potentially impacted wetlands and vegetation in the marshes; and support the crisis response through location of compromised booms and heavily oiled beaches

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

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar

Use of polarimetric SAR data for offshore pollution monitoring is relatively newand shows great potential for operational offshore platformmonitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameterswere extracted fromdifferent types of oil and ‘look-alike’ spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services

A multi-family GLRT-based algorithm for oil spill detection

This paper deals with detection of oil spills from multi-polarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of oil spills in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the oil spills leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is a-priori known the absence of any oil slicks. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant Alarm False Rate (CFAR) property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but non-implementable) structure and some other sub-optimum adaptive detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Detecting covariance symmetries for classification of polarimetric SAR images

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image