Investigating SAR algorithm for spaceborne interferometric oil spill detection

The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 dat

Oil Spill Detection Analyzing “Sentinel 2“ Satellite Images: A Persian Gulf Case Study

Oil spills near exploitation areas and oil loading ports are often related to the ambitions of governments to get more oil market share and the negligence at the time of the loading in large tankers or ships. The present study investigates one oil spill event using multi sensor satellite images in the Al Khafji (between Kuwait and Saudi Arabia) zone. Oil slicks have been characterized with multi sensor satellite images over the Persian Gulf and then analyzed in order to detect and classify oil spills in this zone. In particular this paper discusses oil pollution detection in the Persian Gulf by using multi sensor satellite images data. Oil spill images have been selected by using Sentinel 2 images pinpointing oil spill zones. ENVI software for analysing satellite images and ADIOS (Automated Data Inquiry for Oil Spills) for oil weathering modelling have been used. The obtained results in Al Khafji zone show that the oil spill moves towards the coastline firstly increasing its surface and then decreasing it until reaching the coastline

Oil spill detection using optical sensors: a multi-temporal approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated

Marine Ship Automatic Identification System (AIS) for Enhanced Coastal Security Capabilities: An Oil Spill Tracking Application

National and international trade via shipping is already significant, and expected to continue increasing rapidly over the next decade. Both more ships and larger ships will contribute to this trade, includingships from countries with less rigorous shipping maintenance and inspection standards than the United States, and less strict pollution monitoring regulations. Changes in ship traffic management protocols have been implemented in recent years in the U.S. to minimize damage to coastlines, particularly near sensitive or protected marine environments. For example, to reduce risk to coastal resources off central California, shipping lanes for larger vessels were moved further offshore to allow for additional response time in case of accidents before such vessels might drift into coastal areas. Similarly, shipsare now routed via specific approach channels when entering Boston Harbor to reduce impacts within adjacent National Marine Sanctuary resources. Several recent high profile cases have occurred where \u27mystery\u27 oil spills were found near shipping channels, but no vessel could be readily identified as their source. These incidents lead to extensive and expensive efforts to attempt to identify the shipsresponsible. As time passes in responding to these incidents, the likelihood of confirming the identity of the ships diminishes. Unfortunately, reports of vessels engaging in illegal oily waste discharge to reduce fees for offloading the waste in port are ongoing. We here discuss use of improved capabilities of near-continuous real-time position location monitoring of shipping traffic using marine AutomaticIdentification Systems (AIS) for ships that would facilitate identification of ships responsible for illegal oily waste discharge. The next phase of the National AIS, N-AIS Increment 2, can supply additional spatial coverage not currently included in the N-AIS Increment 1, which can provide an enhanced capability for monitoring shipping and improving managem- ent of coastal ship traffic and response to pollution incidents. These methods will not only improve response time, but reduce cost of response as well

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Observing Marine Pollution with Synthetic Aperture Radar

Non

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

유류오염 탐지를 위한 해양표면 마이크로파 후방산란 분석기반의 준경험적 임계모델

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2023. 8. 김덕진.In an automatic oil spill monitoring system that utilizes SAR satellites, the dark spot detection step, which is responsible for the segmentation of potential oil spills, is undeniably significant. As the initial stage of automatic oil spill detection, this process is typically the most time-consuming and substantially influences the system performance. Considering the vast expanses of ocean that require thorough surveillance, it is crucial to have an efficient method that accurately identifies oil spill candidates at this critical early stage. In this study, a semi-empirical model was carefully proposed, grounded in a comprehensive analysis of the physical characteristics governing the interaction between electromagnetic waves and the sea surface, as well as oil spill observation data from SAR. This model utilizes wind speed, relative wind direction, and incidence angle as independent variables to calculate the threshold radar backscatter coefficient, to differentiate oil spill candidates from the ocean. To determine the parameters of the proposed model, large oil spill observational data was collected from the Sentinel-1 satellite, and the corresponding wind field data was derived from the ECMWF ERA5 reanalysis data. When compared to widely used dark spot detection methods such as the Otsu, Bradley, and active contour methods, the proposed model demonstrated outstanding performance. The model achieved an average F1 score of 0.7948 on the evaluation dataset, while the aforementioned methods showed 0.3315, 0.6400, and 0.5191, respectively. The proposed model exhibited distinguished accuracy with a straightforward implementation process, balancing effectiveness with simplicity, which makes it particularly suitable for real-time oil spill detection where efficiency is paramount. A notable feature of the proposed model is its ability to compute threshold at the pixel-level, unlike conventional patch-level methods that require iterative processes to detect oil spill candidates of varying sizes. This allows the model to identify oil spills in a single operation regardless of their sizes. While the proposed model is flexible in using diverse wind input sources such as buoys, scatterometers, or geophysical model functions, it is crucial to note that its performance depends on the accuracy of the wind field information, specifically, how well it reflects the wind conditions at the exact SAR acquisition time. In conclusion, this study has thoroughly investigated the behavior of the radar backscatter coefficient under both slick-free and slick-covered sea surfaces, leading to the development of a semi-empirical model that can enhance the efficiency of oil spill monitoring systems. The practical implications of the model extend beyond improving system performance; it can be used to create balanced deep-learning datasets by selectively choosing patches with dark spots. Moreover, the physically-grounded nature of the model enables advanced future research, such as distinguishing types of oil or estimating slick thickness.SAR 위성을 활용한 자동 유류오염 모니터링 시스템에서 유류오염 후보를 탐지하는 과정인 dark spot detection 단계는, 시스템의 첫 번째 단계로서 일반적으로 가장 많은 시간이 소요되고, 최종 탐지 성능에 결정적인 영향을 미친다. 넓은 해상 영역을 효과적으로 감시하기 위해서는, 이와 같이 초기 단계에서 유류오염 후보를 정확하고 효율적으로 식별할 필요성이 강조된다. 본 논문에서는 전자기파와 유류막으로 덮인 해양 표면 간의 상호작용에 대한 광범위한 분석과 유류오염 위성 관측값을 기반으로 준경험적 모델을 제시하였다. 제안된 모델은 풍속, 상대 풍향, 입사각을 독립변수로 가지며 바다와 유류오염 후보를 효과적으로 구분하는 레이더 후방산란 계수의 임계값을 산출한다. 모델의 매개변수를 결정하기 위해, Sentinel-1 위성에서 대량의 유류오염 관측 데이터를 수집하였고, 이에 해당하는 바람장 데이터를 ECMWF ERA5 재분석 데이터로부터 추출하여 사용하였다. 본 연구에서 제안한 모델의 segmentation 성능 평가 결과, 평균 F1 점수는 0.7948로 나타났으며, 기존의 대표적인 접근방법인 Otsu, Bradley, active contour model의 성능이 각각 0.3315, 0.6400, 0.5191인 것과 비교하여 우수한 탐지 성능을 보였다. 해당 모델은 물리적 특성을 고려한 직관적인 알고리즘과 높은 segmentation 정확도로, 특히 효율성이 강조되는 실시간 유류오염 모니터링에 매우 적합하다. 또한 제안된 모델은 픽셀 단계의 임계값 계산이 가능하여, 다른 patch 단위로 동작하는 탐지 모델들과 달리, 다양한 크기의 유류오염을 탐지하기 위해 여러 크기의 윈도우를 반복적으로 사용하여 탐지할 필요가 없다. 해당 모델은 해상 부이, 산란계와 같은 다양한 바람장 정보를 입력값으로 사용할 수 있으나, 모델의 성능은 해당 데이터의 정확도에 크게 의존하며, 특히 SAR 이미지 취득 시점의 바람 상태를 얼마나 정확히 반영하는지에 따라 달라진다는 특징을 가진다. 결론적으로, 본 논문에서는 유류오염이 없는 바다 표면과 있는 표면에서의 레이더 후방산란 계수의 변화를 세밀하게 분석하여, 유류오염 모니터링 시스템의 효율성을 강화할 수 있는 준경험적 유류오염 후보 추출 임계값 모델을 개발하였다. 제안된 모델은 모니터링 시스템의 성능을 향상시킬 수 있을 뿐만 아니라, dark spot이 있는 patch를 선별하여 균형잡힌 딥러닝 데이터셋을 생성하는 데에도 활용될 수 있다. 또한, 본 모델은 유류오염과 해양의 물리적 특성에 근거하므로, 유종 식별 또는 유류 두께추정과 같은 후속 연구의 가능성을 제시한다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Literature Review 4 1.3 Research Objective 9 Chapter 2. Microwave Backscattering Properties from the Sea Surface 11 2.1 Microwave backscattering from the Slick-Free Ocean Surface 11 2.1.1 Radar Scattering Model 11 2.1.2 Geophysical Model Function 13 2.2 Microwave backscattering from the Slick-Covered Ocean Surface 18 2.2.1 The Action Balance Equation 18 2.2.2 The Damping Ratio 27 Chapter 3. Development and Validation of the Semi-Empirical Model 29 3.1 Formulation of the Theoretical Framework for the Semi-Empirical Model 29 3.2 Data Acquisition 34 3.2.1 Acquisition of SAR Image Data 34 3.2.2 Acquisition of Wind Field Data 40 3.3 Parameter Determination of the Semi-Empirical Model 44 Chapter 4. Performance Evaluation of the Semi-Empirical Model 47 4.1 Experimental Design 47 4.2 Performance Evaluation with Other Methods 51 4.3 Performance Evaluation for Different Wind Conditions and Regions 60 Chapter 5. Application of the Semi-Empirical Model 69 Chapter 6. Conclusion 72 Bibliography 75 Abstract in Korean 82석