Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

This is an Accepted Manuscript of an article published by Taylor & Francis in Canadian Journal of Remote Sensing on 20 April 2022, available online: https://doi.org/10.1080/07038992.2022.2055534Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil-free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches and that the GC algorithm provides the best optimization.Natural Sciences and Engineering Research Council of Canada (NSERC),Grant RGPIN-2017-04869 || NSERC, Grant DGDND-2017-00078 || NSERC, Grant RGPAS2017-50794 || NSERC, Grant RGPIN-2019-06744

Spatial Modeling of Compact Polarimetric Synthetic Aperture Radar Imagery

The RADARSAT Constellation Mission (RCM) utilizes compact polarimetric (CP) mode to provide data with varying resolutions, supporting a wide range of applications including oil spill detection, sea ice mapping, and land cover analysis. However, the complexity and variability of CP data, influenced by factors such as weather conditions and satellite infrastructure, introduce signature ambiguity. This ambiguity poses challenges in accurate object classification, reducing discriminability and increasing uncertainty. To address these challenges, this thesis introduces tailored spatial models in CP SAR imagery through the utilization of machine learning techniques. Firstly, to enhance oil spill monitoring, a novel conditional random field (CRF) is introduced. The CRF model leverages the statistical properties of CP SAR data and exploits similarities in labels and features among neighboring pixels to effectively model spatial interactions. By mitigating the impact of speckle noise and accurately distinguishing oil spill candidates from oil-free water, the CRF model achieves successful results even in scenarios where the availability of labeled samples is limited. This highlights the capability of CRF in handling situations with a scarcity of training data. Secondly, to improve the accuracy of sea ice mapping, a region-based automated classification methodology is developed. This methodology incorporates learned features, spatial context, and statistical properties from various SAR modes, resulting in enhanced classification accuracy and improved algorithmic efficiency. Thirdly, the presence of a high degree of heterogeneity in target distribution presents an additional challenge in land cover mapping tasks, further compounded by signature ambiguity. To address this, a novel transformer model is proposed. The transformer model incorporates both fine- and coarse-grained spatial dependencies between pixels and leverages different levels of features to enhance the accuracy of land cover type detection. The proposed approaches have undergone extensive experimentation in various remote sensing tasks, validating their effectiveness. By introducing tailored spatial models and innovative algorithms, this thesis successfully addresses the inherent complexity and variability of CP data, thereby ensuring the accuracy and reliability of diverse applications in the field of remote sensing

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

Remote Sensing for International Stability and Security - Integrating GMOSS Achievements in GMES

The Joint Research Centre of the European Commission hosted a two-day workshop "Remote sensing for international stability and security: integrating GMOSS achievements in GMES". Its aim was to disseminate the scientific and technical achievements of the Global Monitoring for Security and Stability (GMOSS) network of excellence to partners of ongoing and future GMES projects such as RESPOND, LIMES, RISK-EOS,PREVIEW, BOSS4GMES, SAFER, G-MOSAIC. The objectives of this workshop were: ¿ To bring together scientific and technical people from the GMOSS NoE and from thematically related GMES projects. ¿ To discuss and compare alternative technical solutions (e.g. final experimental understanding from GMOSS, operational procedures applied in projects such as RESPOND, pre-operational application procedures foreseen from LIMES, etc.) ¿ To draft a list of technical and scientific challenges relevant in the next future. ¿ To open GMOSS to a wider forum in the JRC This report contains abstracts of the fifteen contributions presented by European researchers. The different presentations addressed pre-processing, feature recognition, change detection and applications which represents also the structure of the report. The second part includes poster abstracts presented during a separate poster session.JRC.G.2-Global security and crisis managemen

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

OIL SPILL ALONG THE TURKISH STRAITS SEA AREA; ACCIDENTS, ENVIRONMENTAL POLLUTION, SOCIO-ECONOMIC IMPACTS AND PROTECTION

The Turkish Straits Sea Area (TSSA) is a long water passage that is consisted of the Sea of Marmara, an inland sea within Turkey's borders, and two narrow straits connected to neighboring seas. With a strategic location between the Balkans and Anatolia, the Black Sea and the Mediterranean, and dominated by the continental climate, the region hosted many civilizations throughout the centuries. This makes the region among the busiest routes in the world, with sea traffic three times higher than that in the Suez Canal. The straits are the most difficult waterways to navigate and witnessed many hazardous and important collisions and accidents throughout history. In addition, this area has vital roles as a biological corridor and barrier among three distinctive marine realms. Therefore, the region is rather sensitive to damages of national and international maritime activities, which may cause severe environmental problems. This book addresses several key questions on a chapter basis, including historical accidents, background information on main dynamic restrictions, oil pollution, oil spill detection, and clean-up recoveries, its impacts on biological communities, socioeconomic aspects, and subjects with international agreements. This book will help readers, public, local and governmental authorities gain a deeper understanding of the status of the oil spill, mostly due to shipping accidents, and their related impacts along the TSSA, which needs precautionary measures to be protected.CONTENTS INTRODUCTION CHAPTER I - HISTORY OF ACCIDENTS AND REGULATIONS Remarkable Accidents at the Istanbul Strait Hasan Bora USLUER and Saim OĞUZÜLGEN …………………………………...... 3 History of Regulations before Republican Era along the Turkish Straits Sea Area Ali Umut ÜNAL …………………………………………………………………….. 16 Transition Regime in the Turkish Straits during the Republican Era Osman ARSLAN ……….……………………………………………………….……26 26 The Montreux Convention and Effects at Turkish Straits Oktay ÇETİN ………………………………………………………………….…….. 33 Evaluation of the Montreux Convention in the Light of Recent Problems Ayşenur TÜTÜNCÜ ………………………………………………………………… 44 A Historical View on Technical Developments on Ships and Effects of Turkish Straits Murat YAPICI ………………………………………………………………………. 55 CHAPTER II - GEOGRAPHY, BATHYMETRY AND HYDRO-METEOROLOGICAL CONDITIONS Geographic and Bathymetric Restrictions along the Turkish Straits Sea Area Bedri ALPAR, Hasan Bora USLUER and Şenol AYDIN ……………………..…… 61 Hydrodynamics and Modeling of Turkish Straits Serdar BEJİ and Tarkan ERDİK ………………………………………………….… 79 Wave Climate in the Turkish Sea of Marmara Tarkan ERDİK and Serdar BEJİ …………………………………………………..… 91 CHAPTER III - OIL POLLUTION, DETECTION AND RECOVERY Oil Pollution at Sea and Coast Following Major Accidents Selma ÜNLÜ ……………………………………………………………………….101 Forensic Fingerprinting in Oil-spill Source Identification at the Turkish Straits Sea Area Özlem ATEŞ DURU ……………………………………………………………… 121 xi Oil Spill Detection Using Remote Sensing Technologies-Synthetic Aperture Radar (SAR) İbrahim PAPİLA, Elif SERTEL, Şinasi KAYA and Cem GAZİOĞLU ……..……. 140 The Role of SAR Remote Sensing to Detect Oil Pollution and Emergency Intervention Saygın ABDIKAN, Çağlar BAYIK and Füsun BALIK ŞANLI ……….….……….. 157 Oil Spill Recovery and Clean-Up Techniques Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………… 176 Turkish Strait Sea Area, Contingency Planning, Regulations and Case Studies Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………... 188 Dispersant Response Method to Incidental Oil Pollution Dilek EDİGER, Leyla TOLUN and Fatma TELLİ KARAKOÇ ………………….... 205 CHAPTER IV - THE EFFECTS / IMPACTS OF OIL SPILL ON BIOLOGICAL COMMUNITIES – INCLUDING SAMPLING AND MONITORING Marine Microorganisms and Oil Spill Sibel ZEKİ and Pelin S. ÇİFTÇİ TÜRETKEN …………...………………………… 219 Estimated Effects of Oil Spill on the Phytoplankton Following “Volgoneft-248” Accident (Sea of Marmara) Seyfettin TAŞ ………………………………..…………………………………….... 229 Interactions between Zooplankton and Oil Spills: Lessons Learned from Global Accidents and a Proposal for Zooplankton Monitoring İ. Noyan YILMAZ and Melek İŞİNİBİLİR ……………………………………..….. 238 The Effects of Oil Spill on the Macrophytobenthic Communities Ergün TAŞKIN and Barış AKÇALI …………………………….…………….……. 244 Potential Impacts of Oil Spills on Macrozoobenthos in the Turkish Straits System Güley KURT-ŞAHİN …………………………………………………………….… 253 The Anticipated Effects of Oil Spill on Fish Populations in Case of an Accident along the Turkish Straits System – A review of Studies after Several Incidents from the World M. İdil ÖZ and Nazlı DEMİREL …………………………………………………….261 Estimated Impacts of an Oil Spill on Bird Populations along the Turkish Straits System Itri Levent ERKOL …………………………………………………………….…… 272 The Effect of Oil Spills on Cetaceans in the Turkish Straits System (TSS) Ayaka Amaha ÖZTÜRK ………………………………………………………….. 277 Changes in the Ichthyoplankton and Benthos Assemblages following Volgoneft-248 Oil Spill: Case Study Ahsen YÜKSEK and Yaprak GÜRKAN …………………………………….……. 280 Assessing the Initial and Temporal Effects of a Heavy Fuel Oil Spill on Benthic Fauna Yaprak GÜRKAN, Ahsen YÜKSEK ………………………………………..…….. 287 CHAPTER V - SOCIO-ECONOMIC ASPECTS Socio-economic Aspects of Oil Spill Özlem ATEŞ DURU and Serap İNCAZ ……………………………………….…… 301 Effects of Oil Spill on Human Health Türkan YURDUN ………………………………………………………………..…. 313 Crisis Management of Oil Spill, A Case Study: BP Gulf Mexico Oil Disaster Serap İNCAZ and Özlem ATEŞ DURU …………………………….………….……324 CHAPTER VI - CONVENTIONS RELATING TO PREVENTION OF OIL SPILL International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL), 1954 and its Situation Related with Turkey Emre AKYÜZ, Metin ÇELİK and Ömer SÖNER …………………………...……... 334 International Convention for the Prevention of Pollution from Ships, 1973, as Modified by the Protocol of 1978 Relating Thereto and by the Protocol of 1997 (MARPOL) Özcan ARSLAN, Esma UFLAZ and Serap İNCAZ ………………………….……. 342 Applications of MARPOL Related with Oil Spill in Turkey Emre AKYÜZ, Özcan ASLAN and Serap İNCAZ ………………………………… 356 Ship Born Oil Pollution at the Turkish Straits Sea Area and MARPOL 73/78 Duygu ÜLKER and Sencer BALTAOĞLU………………………….…………….. 363 International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties (INTERVENTION 1969) and its Applications Related with Oil Spill in Turkey Şebnem ERKEBAY ……………………………….……………………………….. 371 International Convention on Oil Pollution Preparedness, Response and Co-operation (OPRC) 1990 and its Applications Related with Oil Spill in Turkey Kadir ÇİÇEK ………………………………………………………………………. 381 Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 (OPRC-HNS Protocol) and its Effects in Turkey Aydın ŞIHMANTEPE and Cihat AŞAN ……………….…………………………. 392 The International Convention on Salvage (SALVAGE) 1989 Related with Oil Spill in Turkey İrşad BAYIRHAN ……………………………………….………………..……….. 408 CHAPTER VII - CONVENTIONS COVERING LIABILITY AND COMPENSATION RELATED WITH OIL SPILL International Convention on Civil Liability for Oil Pollution Damage (CLC), 1969 and its Applications Serap İNCAZ and Pınar ÖZDEMİR ……………………………………..………… 416 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage (FUND 1992) and its Applications Related with Oil Spill in Turkey Ali Umut ÜNAL and Hasan Bora USLUER …………………………….………… 424 International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea (HNS), 1996 (and its 2010 Protocol) and its Applications Related with Oil Spill in Turkey Bilun ELMACIOĞLU ……………………………………………………………… 437 Bunkering Incidents and Safety Practices in Turkey Fırat BOLAT, Pelin BOLAT and Serap İNCAZ …………………………………... 447 "Nairobi International Convention on the Removal of Wrecks 2007" and its Effects on Turkey Şafak Ümit DENİZ and Serap İNCAZ ……………………….……………………. 457

Hydrocarbon quantification using neural networks and deep learning based hyperspectral unmixing

Hydrocarbon (HC) spills are a global issue, which can seriously impact human life and the environment, therefore early identification and remedial measures taken at an early stage are important. Thus, current research efforts aim at remotely quantifying incipient quantities of HC mixed with soils. The increased spectral and spatial resolution of hyperspectral sensors has opened ground-breaking perspectives in many industries including remote inspection of large areas and the environment. The use of subpixel detection algorithms, and in particular the use of the mixture models, has been identified as a future advance that needs to be incorporated in remote sensing. However, there are some challenging tasks since the spectral signatures of the targets of interest may not be immediately available. Moreover, real time processing and analysis is required to support fast decision-making. Progressing in this direction, this thesis pioneers and researches novel methodologies for HC quantification capable of exceeding the limitations of existing systems in terms of reduced cost and processing time with improved accuracy. Therefore the goal of this research is to develop, implement and test different methods for improving HC detection and quantification using spectral unmixing and machine learning. An efficient hybrid switch method employing neural networks and hyperspectral is proposed and investigated. This robust method switches between state of the art hyperspectral unmixing linear and nonlinear models, respectively. This procedure is well suited for the quantification of small quantities of substances within a pixel with high accuracy as the most appropriate model is employed. Central to the proposed approach is a novel method for extracting parameters to characterise the non-linearity of the data. These parameters are fed into a feedforward neural network which decides in a pixel by pixel fashion which model is more suitable. The quantification process is fully automated by applying further classification techniques to the acquired hyperspectral images. A deep learning neural network model is designed for the quantification of HC quantities mixed with soils. A three-term backpropagation algorithm with dropout is proposed to avoid overfitting and reduce the computational complexity of the model. The above methods have been evaluated using classical repository datasets from the literature and a laboratory controlled dataset. For that, an experimental procedure has been designed to produce a labelled dataset. The data was obtained by mixing and homogenizing different soil types with HC substances, respectively and measuring the reflectance with a hyperspectral sensor. Findings from the research study reveal that the two proposed models have high performance, they are suitable for the detection and quantification of HC mixed with soils, and surpass existing methods. Improvements in sensitivity, accuracy, computational time are achieved. Thus, the proposed approaches can be used to detect HC spills at an early stage in order to mitigate significant pollution from the spill areas

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster–Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces