16 research outputs found
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
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture
radar (TSX SC) images from November 2019 to March 2020,
covering the Multidisciplinary drifting Observatory for the
Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and
relatively high spatial resolution of TSX SC data and is a
useful basic dataset for future MOSAiC studies on physical
sea ice processes and ocean and climate modeling. Sea ice is
classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with
different degrees of deformation. We establish the per-class
incidence angle (IA) dependencies of TSX SC intensities
and gray-level co-occurrence matrix (GLCM) textures and
use a classifier that corrects for the class-specific decreasing
backscatter with increasing IAs, with both HH intensities and
textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while
maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier
are adjusted by Markov random field contextual smoothing
to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 %
and good correspondence to geometric ice surface roughness
derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive
logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of
classes representing ice openings (leads and young ice) show
prominent increases in middle to late November 2019 and
March 2020, corresponding well to ice-opening time series
derived from in situ data in this study and those derived from
satellite synthetic aperture radar (SAR) and optical data in
other MOSAiC studies
Texture analysis and Its applications in biomedical imaging: a survey
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity
and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications.
This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021;
date of current version January 24, 2022. This work was supported in
part by the Portuguese Foundation for Science and Technology (FCT)
under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by
FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio
Sea Ice Mapping in Labrador Coast with Sentinel-1 Synthetic Aperture Radar Imagery
Sea ice mapping is crucial to Canadian coast, including marine transportation, environmental protection, resource management, disaster and emergency management, especially under current background of climate change. Canadian RADARSAT-2, like other synthetic aperture radar (SAR) sensors, is an essential source for current sea ice mapping in Canada, However, its limited revisiting makes daily ice chart generation challenging. The RADARSAT Constellation project is expected to be launched in 2018, the gap of data availability is expected to be filled with imagery from multiple sources. Sentinel-1, launched by European Space Agency (ESA) in late 2014, is an alternative source for sea ice mapping with comparable capability of RADARSAT-2 in wide swath mode. The main objective of this study is to examine the performance of Sentinel-1 imagery in sea ice mapping with a semi-automated image segmentation workflow.
The methodology consists of two main steps. First, the most significant features in sea ice interpretation were determined using a random forest feature selection method. Second, an unsupervised graph-cut image segmentation is performed.
The workflow was tested on 15 dual-polarized Sentinel-1A Extra Wide (EW) scenes in Labrador coast from December, 2015 to June, 2016, and the results were evaluated on the accuracy of water segmentation. The study found that: 1) GLCM features are effective in distinguishing different ice classes and 6 most important features were selected; 2) the proposed semi-automated workflow is able to segment Sentinel-1 imagery into 3 to 8 classes for water identification; and 3) generally Sentinel-1 imagery has similar responses from first-year ice compared with previous sensors, but with a different noise pattern in cross-polarized bands; and the overall accuracy of water identification reached close to 95%
Classification of Compact Polarimetric Synthetic Aperture Radar Images
The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis.
Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping.
This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data.
The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation
Automated Remote Sensing Image Interpretation with Limited Labeled Training Data
Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science.
Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month
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
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc