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

Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers

We exploit the alternating direction method of multipliers (ADMM) for developing an oil spill segmentation method, which effectively detects oil spill regions in blurry synthetic aperture radar (SAR) images. We commence by constructing energy functionals for SAR image deblurring and oil spill segmentation separately. We then integrate the two energy functionals into one overall energy functional subject to a linear mapping constraint that correlates the deblurred image and the segmentation indicator. The overall energy functional along with the linear constraint follows the form of alternating direction method of multipliers and thus enables an effective augmented Lagrangian optimization. Furthermore, the iterative updates in the ADMM maintain information exchanges between the energy minimizations for SAR image deblurring and oil spill segmentation. Most existing blurry image segmentation strategies tend to consider deblurring and segmentation as two independent procedures with no interactions, and the operation of deblurring is thus not guided for obtaining accurate segmentation. In contrast, we integrate deblurring and segmentation into one overall energy minimization framework with information exchanges between the two procedures. Therefore, the deblurring procedure is inclined to operate in favor of more accurate oil spill segmentation. Experimental evaluations validate that our framework outperforms the separate deblurring and segmentation strategy for detecting oil spill regions in blurry SAR images

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