33 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Sustainable marine ecosystems: deep learning for water quality assessment and forecasting
An appropriate management of the available resources within oceans and coastal regions is
vital to guarantee their sustainable development and preservation, where water quality is a key element.
Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet
of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim.
In this paper, we review methodologies and technologies for water quality assessment that contribute to a
sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for
water quality estimation and forecasting. The analyzed literature is classified depending on the type of task,
scenario and architecture. Moreover, several applications including coastal management and aquaculture
are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where
transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies
are expected to be the main involved agents.Postprint (published version
HAZOP: Our Primary Guide in the Land of Process Risks: How can we improve it and do more with its results?
PresentationAll risk management starts in determining what can happen. Reliable predictive analysis is key. So, we perform process hazard analysis, which should result in scenario identification and definition. Apart from material/substance properties, thereby, process conditions and possible deviations and mishaps form inputs. Over the years HAZOP has been the most important tool to identify potential process risks by systematically considering deviations in observables, by determining possible causes and consequences, and, if necessary, suggesting improvements. Drawbacks of HAZOP are known; it is effort-intensive while the results are used only once. The exercise must be repeated at several stages of process build-up, and when the process is operational, it must be re-conducted periodically. There have been many past attempts to semi- automate the HazOp procedure to ease the effort of conducting it, but lately new promising developments have been realized enabling also the use of the results for facilitating operational fault diagnosis. This paper will review the directions in which improved automation of HazOp is progressing and how the results, besides for risk analysis and design of preventive and protective measures, also can be used during operations for early warning of upcoming abnormal process situations
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
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
How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective
Artificial intelligence experienced a technological breakthrough in science,
industry, and everyday life in the recent few decades. The advancements can be
credited to the ever-increasing availability and miniaturization of
computational resources that resulted in exponential data growth. However,
because of the insufficient amount of data in some cases, employing machine
learning in solving complex tasks is not straightforward or even possible. As a
result, machine learning with small data experiences rising importance in data
science and application in several fields. The authors focus on interpreting
the general term of "small data" and their engineering and industrial
application role. They give a brief overview of the most important industrial
applications of machine learning and small data. Small data is defined in terms
of various characteristics compared to big data, and a machine learning
formalism was introduced. Five critical challenges of machine learning with
small data in industrial applications are presented: unlabeled data, imbalanced
data, missing data, insufficient data, and rare events. Based on those
definitions, an overview of the considerations in domain representation and
data acquisition is given along with a taxonomy of machine learning approaches
in the context of small data
SSA-SiamNet:Spectral-Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection
Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral-spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral-spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports