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

Observing Marine Pollution with Synthetic Aperture Radar

Non

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

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

An investigation on the damping ratio of marine oil slicks in synthetic aperture radar imagery

The damping ratio has recently been used to indicate the relative internal oil thickness within oil slicks observed in synthetic aperture radar (SAR) imagery. However, there exists no well-defined and evaluated methodology for calculating the damping ratio. In this study, we review prior work regarding the damping ratio and outline its theoretical and practical aspects. We show that the most often used methodology yields damping ratio values that differ, in some cases significantly, for the same scene. Three alternative methods are tested on multi-frequency data sets of verified oil slicks acquired from DLR's F-SAR instrument, NASA's Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Sentinel-1. All methods yielded similar results regarding relative thickness variations within slick. The proposed damping ratio derivation methods were found to be sensitive to the proportion of oil covered pixels versus open water pixels in the azimuth direction, as well as to the scene size in question. We show that the fully automatable histogram method provides the most consistent results even under challenging conditions. Comparisons between optical imagery and derived damping ratio values using F-SAR data show good agreement between the relatively thicker oil slick areas for the two different types of sensors

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Technical approaches, chapter 3, part E

Radar altimeters, scatterometers, and imaging radar are described in terms of their functions, future developments, constraints, and applications

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments

Multifractal observations of eddies, oil spills and natural slicks in the ocean surface

Natural and man-made distributions of tensioactive substance concentrations in the sea surface features exhibit self-similarity at all radar reflectivity levels when illuminated by SAR. This allows the investigation of the traces produced by vortices and other features in the ocean surface. The man-made oil spills besides often presenting some linear axis of the pollutant concentration produced by moving ships also show their artificial production in the sea surface by the reduced range of scales, which widens as time measured in terms of the local eddy diffusivity distorts the shape of the oil spills. Thanks to this, multifractal analysis of the different backscattered intensity levels in SAR imagery can be used to distinguish between natural and man-made sea surface features due to their distinct self-similar properties. The differences are detected using the multifractal box-counting algorithm on different sets of SAR images giving also information on the age of the spills. Different multifractal algorithms are compared presenting the differences in scaling as a function of some physical generating process such as the locality or the spectral energy cascade