Table Of ContentAutomatic Detection
Algorithms of Oil Spill in
Radar Images
Maged Marghany
Director Institute of Geospatial Applications
Faculty Geospatial and Real Estate
Geomatika University College
Kuala Lumpur, Malaysia
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A SCIENCE PUBLISHERS BOOK
A SCIENCE PUBLISHERS BOOK
Cover illustrations provided by the author of the book, Dr. Maged Marghany
CRC Press
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Dedicated to
My Mother Faridah
and
Nikola Tesla and Richard Feynman who taught me that
real works of professors last forever while fake works of
professors do not.
Preface
These days, the new generation of Synthetic Aperture Radar (SAR) sensors necessitates
the growth of new procedures for trustworthy data processing and information
abstraction. Yet no work has bridged the gap between modern physics and complete
understanding of microwave theories. Indeed, microwave remote sensing theories and
techniques are based on modern physics. Moreover, there is also a great gap between
modern applied physics and SAR data image processing. In this view, the main image
processing techniques of SAR data is restricted by speckle filter procedures, classical
edge detection tools and mathematical arithmetic operation, which, especially applies
to SAR polarimetry data processing. The growth in SAR data processing, mainly
for automatic detection of oil spill shows slow development in term of algorithms.
The majority of automatic detection oil spill algorithms to SAR data are restricted
conventional image processing tools, which are based on classical image processing
techniques such as image segmentation and classical learning machine algorithm,
for instance, artificial neural networks and support vector machine algorithms. Yet
these procedures are not able to simulate the trajectory movement of an oil spill or to
forecast its path from a single SAR data or multiSAR data.
Recently, SAR data have proven the great potential for monitoring and tracking
the Deepwater Horizon oil spill disasters. However, the SAR data are only used for
automatic detection of an oil spill without demonstrating the gradient variation across
the spill-covered water, i.e., oil spill spreading in SAR data. In this view, Synthetic
Aperture Radar Imaging Mechanism for Oil Spills delivers the critical tool needed to
understand the latest technology in radar imaging of oil spills, particularly microwave
radar as the main cradle to monitor and precisely detect marine oil pollution. To this end,
modern physics such as quantum mechanics must be involved in microwave theories
and their data processing. This might lead to a new era of the quantum microwave,
quantum computing and quantum image processing.
The aim of this book is to viaduct the mismatch between modern physics, quantum
mechanism and applications of radar imaging and automatic detection algorithm of
the oil spill. This book is divided into mechanical details to assist in the potentiality
of synthetic aperture radar (SAR) and key approaches to be depleted to extricate the
worth-novel information crucial, for instance, location, size, perimeter and chemical
vi Automatic Detection Algorithms of Oil Spill in Radar Images
details of the oil spill from SAR measurements. Rounding out with practical simulation
rajectory movement of oil spills using radar images, Synthetic Aperture Radar Imaging
Mechanism and processing for Oil Spills conveys an operative novel stoolpigeon of
modern machinery and is used by present day oil and marine pollution engineers.
This book delivers a comprehensive understanding of Maxwell’s equations. In fact,
these equations are the keystone to understanding the speculations of the microwave
remote sensing. Truly, the common microwave theories are imperative to comprehend
the physics of Maxwell’s equations. However, the majority of graduates of geospatail
and microwave specialists no longer correlate between microwave remote sensing
and Maxwell’s equations. In this circumstance, the foremost standing is how to run a
variety of SAR filter techniques to minimize speckles. In general, mapping is a general
output product based on microwave data without grasping the key theory behind
microwave remote sensing. Consequently, the copiously comprehensive Maxwell’s
equations are described in Chapter 1.
It is impossible to deal with photons as a basic of microwave remote sensing
without understanding their mechanical behavior. Chapter 2 reveals the quantum
mechanics theories that explain in depth the behavior of photons as a core of
electromagnetic as a function of Maxwell’s equations. In continuation with
Chapter 2, Chapter 3 demonstrates the novel theory of Josephson junctions to understand
the behavior of microwave photons. While, Chapter 4 describes the scattering theory
from the point view of the quantum mechanics. In this view, the quantum radar
theories can be established. State-of-the-Art, professor Dr. Marco Lanzagorta is the
only pioneering scientist who associated quantum mechanism to Maxwell’s equations
and radar imaging techniques. The speculative of the quantum radar mechanisms,
consequently, is initially delivered to originate the novel decoherence quantum
theory of oil spill imaging in Synthetic Aperture Radar in Chapter 5. However, the
conventional principle of Synthetic Aperture Radar is also addressed in Chapter 6
to fill the gap between quantum radar theories and conventional SAR techniques.
Moreover, the relativity theories are also involved in understanding the radar image
mechanism. Chapter 8 also reveals the quantization of oil spill imaging mechanism
in synthetic aperture radar.
The book delivers conventional algorithms for automatic detection of oil spills.
For instance, texture algorithms and conventional machine learning, i.e., Mahalanobis,
neural network and fractal algorithms are described in Chapters 9, 10, and 11,
respectively. However, Chapter 9 introduces a novel algorithm, which is based on the
quantum entropy for automatic detection of oil spill.
The book also delivers a new approach for automatic detection of the oil spill
by implementing a new approach of Quantum-dot Cellular Automata, which has
never been implemented before this book. A more advanced study and a new work of
quantum multiobjective algorithm is presented in Chapter 13 for automatic detection
of oil spill spreading in full polarimetric SAR data. The last chapter introduces a
novel approach for simulation and forecasting oil spill trajectory movements based on
Preface vii
quantum Hopfield algorithm. This algorithm is implemented with multiSAR satellite
data. The quantum Hopfield achieves automatic detection of oil spill spreading and
forecasts the oil spill trajectory movement over different time of SAR data acquision.
Prof. Dr. Maged Marghany
Microwave Remote Sensing Expert
Director Institute of Geospatial Applications
Faculty Geospatial and Real Estate,
Geomatika University College,
Taman Setiawangsa, 54200,
Kuala Lumpur, WP Kuala Lumpur,
Malaysia
Contents
Preface v
1. Microwave Remote Sensing Based on Maxwell Equations 1
1.1 Maxwell’s Equations 1
1.2 Simple Wave Equation Based on Maxwell’s Equations 4
1.3 Solution of Electromagnetic Waves in a Homogenous Dielectric 5
1.4 Electromagnetic Wave Characteristics Based on Maxwell’s Equations 7
1.5 The Poynting Theory 9
1.6 Waves from Localized Sources 11
1.7 Momentum as the Route of Radiation Pressure 12
1.8 Can Maxwell’s Electrodynamics Formulate in Space and Time? 12
2. Quantization of Maxwell’s Equations and Electromagnetic Field 15
2.1 Definitions of Quantization of Electromagnetic Field 15
2.2 Quantum Radiation 16
2.3 The Photoelectric Effect 16
2.4 De Broglie’s Wavelength 18
2.5 Quantum Electrodynamics 19
2.6 Force Carriers 19
2.7 Maxwell Photon Wave Function 22
2.8 Quantanize of Electromagnetic Waves 25
2.9 Feynman’s Perspective of Electromagnetic Waves 25
2.10 Feynman’s Derivation of Maxwell’s Equations 28
2.11 Photon Spins 30
2.12 Do Maxwell’s Equations Describe a Single Photon or an Infinite 31
Number of Photons?
3. Quantum Signals at Microwave Devices 32
3.1 Electromagnetic Wave and Microwave Beam 32
3.2 Photon of Microwave Beams 34
3.3 Concept of Generating Microwave Beams 35
3.4 Josephson Junctions for Microwave Photon Generations 37
3.5 Mathematical Description of Quantum Microwave 40
3.6 Microwave Signal Harmonic Oscillators Using Ladder Operator 41
3.7 Quantum Electromagnetic Signals Propagating Along 42
Transmission Lines
3.8 Quantum Langevin Equation 44
