Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modeling

Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modelling is both a research and practice-based reference that bridges the gap between the remote sensing field and the dynamic oceanography exploration field. In this perspective, the book explicates how to apply techniques in synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for oceanic turbulence and front simulation and modeling. It includes detailed algorithms to enable readers to better understand and implement practices covered in their own work and apply QInSAR to their own research.

This multidisciplinary reference is useful for researchers and academics in dynamic oceanography and modeling, remote sensing and aquatic science, as well as geographers, geophysicists, and environmental engineers.

Cover image
Title page
Table of Contents
Copyright
Dedication
Preface
Chapter 1. Nonlinearity: fundamental concepts and understanding
Abstract
1.1 What is the magic of nonlinearity?
1.2 Nonlinearity and dynamics
1.3 Mathematical definition of nonlinearity
1.4 Sort of nonlinear functions
1.5 Diagnosis of a nonlinear system
1.6 Nonlinear differential equations
1.7 Nonlinearity singularity: emerging in small scale
1.8 Bifurcation and discontinuity
1.9 Can nonlinearity criticality: escape from linearity?
1.10 Continuity of nonlinearity
1.11 Lipschitz continuity
1.12 Monotonicity
References
Chapter 2. Unraveling the mysteries: Ocean turbulence and front dynamics
Abstract
2.1 What is the magic meaning of turbulence?
2.2 What is the origin of ocean turbulence?
2.3 What is the role of Reynolds number in understanding turbulence mechanisms?
2.4 How can laminar flow and turbulent flow be distinguished?
2.5 What is the nature of turbulence?
2.6 Boundary layers in turbulence
2.7 Turbulence equation of motion
2.8 Navier−Stokes equations and the turbulent shear stress equation
2.9 Reynolds-averaged Navier–Stokes equations
2.10 Boussinesq approximation (Buoyancy)
2.11 Turbulence closure problem and eddy viscosity
2.12 Mixing length model
2.13 Spectra of turbulence
2.14 Geostrophic turbulence
2.15 How do frontal zones and turbulence primarily interact?
2.16 How are frontal zones defined?
2.17 Formation of fronts
2.18 How do temperature and salinity accurately describe frontal zones?
References
Chapter 3. Quantized synthetic aperture radar signal: a comprehensive exploration
Abstract
3.1 What is quantization?
3.2 What distinguishes first from second quantization?
3.3 How and why are photons quantized?
3.4 How can electromagnetic waves become quantized?
3.5 Quantazied microwave signal
3.6 Quantum microwave dynamics: unveiling the journey of propagation
3.7 Quantum microwaves and resonators: a symbiotic dialog
3.8 Quantum microwave detection: unraveling the quantum realm
3.9 Quantum insights into radar quantization
3.10 Advancements in quantum radar: illumination strategies and radar equation formulation
3.11 Synergizing quantum illumination and radar equations through entanglement dynamics
3.12 Exploring quantum principles in synthetic aperture radar systems
3.13 Quantum mechanics of radar cross section backscatter from Bragg wave turbulence surface
3.14 Quantum pulse-compression ranging: unveiling the secrets of quantum radar signal processing
3.15 Synthetic aperture radar satellite sensors
References
Chapter 4. Quantized Marghany turbulence boundary in the South China Sea by using SAR data
Abstract
4.1 The South China Sea: Why?
4.2 What is the boundary current turbulent flow?
4.3 Sverdrup relation
4.4 Stommel model
4.5 What are the key distinctions between Stommel mode and Severdrup balance?
4.6 Munk solutions
4.7 South China Sea boundary turbulence current flows
4.8 Oceanographic data acquisitions
4.9 Driving quantized Marghany turbulence boundary current flows theory
4.10 Tracking the South China Sea quantized Marghany turbulence boundary states
4.11 Synthetic aperture radar quantized Marghany turbulence boundary imaging algorithm
4.12 Quantized Marghany turbulence spectra energy along the western boundary of Malaysia’s East Coast in SAR images
4.13 Seasonal quantized Marghany turbulence spectra energy in SAR images
References
Chapter 5. Automatic detection of internal wave in quantized Marghany turbulence boundary in SAR data
Abstract
5.1 What constitutes the foundation of the internal waves?
5.2 How do gravity waves form internal waves?
5.3 Mathematical foundation in describing internal wave
5.4 Physics of internal waves
5.5 What impact does internal wave propagation have on the mean flow?
5.6 Continuous stratification and rotation
5.7 Quantization of internal waves
5.8 Radar imaging mechanism of internal waves
5.9 Hermitian wavelets for internal wave detection in SAR image
5.10 Automatic detection of internal waves using quantum particle swarm optimization algorithm
5.11 Why do internal waves occur in the quantized Marghany turbulence boundary?
5.12 What are the distinctions between X-band and C-band in imaging internal waves?
5.13 Energy spectral of internal wave turbulence in MultiSAR data
References
Chapter 6. Quantized Marghany oceanic front detection in MultiSAR datasets: unveiling the hidden patterns of the South China Sea
Abstract
6.1 What is the magic of oceanic front?
6.2 What are the differences between oceanic and atmospheric fronts?
6.3 Oceanic front types
6.4 Timescale of dissipation of an upper oceanic front
6.5 Dissipation frontal model formulation
6.6 Equation of motion
6.7 Viscous perturbation solution
6.8 How does Ekman number impact oceanic front?
6.9 Quantized Marghany oceanic front
6.10 Theoretical quantized Marghany oceanic front imaging in SAR data
6.11 Quantum edge detection algorithm for quantized Marghany bounded states in SAR data
6.12 Can a quantum edge detection algorithm locate various kinds of quantized Marghany bound front states in SAR data?
6.13 Spectral of quantized Marghany bound front state
6.14 Why can algorithms for quantum image fusion and quantum edge detection automatically identify the boundaries in different images?
References
Further Reading
Chapter 7. Texture and quantum entropy algorithms for turbulence and oceanic front structural detections in synthetic aperture radar images
Abstract
7.1 What is the significance of speckles in revealing turbulence front features in SAR data?
7.2 Formation of speckles
7.3 How can the Hamiltonian operator examine the energy dispersion within a speckle pattern spanning the quantized Marghany turbulence boundary?
7.4 Multilook processing and speckle
7.5 Quantized multilook processing
7.6 Types of speckle noises
7.7 White and Gaussian noises
7.8 Dynamic speckles
7.9 SAR image turbulence textures
7.10 Texture synthetic aperture radar image algorithms
7.11 What relationship exists between GLCM and the pixels within SAR images?
7.12 How does GLCM function within the context of SAR turbulence image analysis?
7.13 How the potential of GLCM transforms a SAR turbulence image into a symmetrical matrix?
7.14 How to normalize symmetrical and nonsymmetrical SAR GLMC matrix?
7.15 How can we generate an SAR turbulence texture image?
7.16 Mathematical form of cooccurrence matrix
7.17 Entropy
7.18 Why can homogeneity detect multiple phases of quantized Marghany bound front states?
7.19 Quantum entropy
References
Chapter 8. Developing a novel quantum cluster algorithm for the automatic detection of spiral patterns in SAR data
Abstract
8.1 What is meant by spirals?
8.2 Distinguishing between “spiral,” “eddy,” and “vorticity” is essential in fluid dynamics
8.3 Formation mechanism of spirals
8.4 Mathematical description of spirals
8.5 Geometric properties of spirals
8.6 Numerical solution of spirals on the sea
8.7 How does SAR imagine spiral patterns
8.8 Automatic detection of spirals in SAR images
8.9 Identifying spiral feature pattern in SAR data
8.10 Tested SAR data
8.11 Theory of quantized spiral turbulence
8.12 What makes Quantum_Superposition_MDD a valuable tool for automated spiral detection?
References
Chapter 9. Fractal dimension algorithm for automatic detection of oceanic front and turbulence structural in MultiSAR data
Abstract
9.1 What is a magic of fractal art?
9.2 Classical fractals
9.3 Statistical self-similarity
9.4 Fractal dimension
9.5 Multifractal systems
9.6 Calculating multifractal scaling using box counting
9.7 What is the relationship between turbulence and multifractals?
9.8 Wavelet-based multifractal analysis for turbulence
9.9 Automatic determination of turbulence front types in MultiSAR data
9.10 Quantitative turbulence zones using singularity spectrum
References
Chapter 10. Quantum finite automaton-based algorithm for identifying rain cell turbulences in synthetic aperture radar images
Abstract
10.1 Rain cells
10.2 Rain cell shapes and orientations
10.3 Formation mechanisms of rain cells
10.4 How can rainfall generates turbulence across the surface of the ocean?
10.5 Quantized rain cells interaction with sea surface
10.6 Quantanized Marghany rain interaction coefficient
10.7 Radar signature of rain cells on the sea surface
10.8 Quantanization of rain cell impact on radar backscatter
10.9 A quantum finite automaton algorithm
10.10 Automatic detection of rain cells in synthetic aperture radar imagery
10.11 Quantum operator gate
10.12 In situ measurement and synthetic aperture radar satellite data
10.13 Automatic detection of rain cells in synthetic aperture radar image
10.14 Rain cells' training score and the bias of the automated classifier
References
Chapter 11. Quantum cellular automata algorithm for automatic detection of upwelling in synthetic aperture radar data
Abstract
11.1 What is upwelling?
11.2 Impacts of upwelling
11.3 Artificial upwelling
11.4 Why Coriolis effect is keystone of upwelling?
11.5 Quantization of Coriolis effects
11.6 Mechanism of upwelling
11.7 Ekman spiral
11.8 Ekman number
11.9 Ekman velocity
11.10 Upwelling index
11.11 Quantized Ekman spiral
11.12 Introducing a novel theory: quantum-enhanced coastal upwelling imaging
11.13 Quantum cellular automata for automatic upwelling in synthetic aperture radar data
11.14 Quantum-inspired adder circuit for automated upwelling detection
11.15 Quantum cellular automata for automated detection of upwelling zones
11.16 Incorporating quantum cellular automata for the automated identification of upwelling zones through in situ measurements
11.17 Island wake
11.18 Why quantum cellular automaton can detect potential zone of upwelling automatically?
References
Further Reading
Chapter 12. Quantum multiobjective algorithm for detecting turbulence vorticity and eddies in synthetic aperture radar satellite data
Abstract
12.1 What is meant by vortex?
12.2 What are different between eddy and vortex?
12.3 Geometry of eddies and vortices: a comprehensive exploration of fluidic phenomena
12.4 Mathematical formulation of eddies and vorticities: a comprehensive description
12.5 Unveiling abstract mechanisms underlying eddy and vorticity in synthetic aperture radar data
12.6 Developing a backscatter energy model for synthetic aperture radar imaging mechanisms involving vorticity and eddy roughness
12.7 Automated detection of turbulent flows in synthetic aperture radar images using quantum algorithms
12.8 Quantum-enhanced synthetic aperture radar image processing for automated eddy and vorticity detection
12.9 Quantum multiobjective evolutionary algorithm
12.10 Population pattern of quantum eddy and vorticity turbulent flow generations
12.11 Pareto optimal solution for quantum nondominated sort and elitism
12.12 Boundary condition of quantum probability in tracking eddy and vorticity turbulence flows in synthetic aperture radar data
12.13 Automated identification of turbulent eddy and vorticity patterns in synthetic aperture radar imagery
12.14 The significance of Pareto optimization in QNSGA-II
12.15 Quantum coherence in synthetic aperture radar imaging: exploring turbulent flow magnitudes with QNSGA-II
References
Chapter 13. Four-dimensional quantum holographic interferometry radar for nonlinear shear-wave chaotic flow detection
Abstract
13.1 What constitutes chaos, and is turbulence synonymous with chaotic behavior?
13.2 Exploring chaotic turbulence in the ocean: a contextual perspective
13.3 Initiating turbulence via shear waves: mechanisms and implications
13.4 Exploring the dynamics of chaotic advection: a comprehensive characterization
13.5 Expansion of filaments versus the development of tracer gradients
13.6 Types of radar interferometry: an exploration
13.7 Challenges in InSAR data processing: an exploration
13.8 Speculation for quantization of hologram interferometry
13.9 Marghany 4D quantized hologram interferometry algorithm
13.10 Conducting measurements in the natural environment
13.11 TanDEM-X satellite data
13.12 4-D shear-wave chaotic turbulence through hologram interferometry on TanDEM-X satellite
13.13 Quantum relativity: four-dimensional sea surface reconstruction in TanDEM data
References
Index

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Synthetic Aperture Radar Image Processing Algorithms for Nonlinear Oceanic Turbulence and Front Modeling

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