-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Machine Learning in Biomedical Science and Healthcare Informatics
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalRecognition and Perception of Images
 | Fundamentals and Applications Edited by Iftikhar B. Abbasov Copyright: 2021 | Expected Pub Date:2021/02/25 ISBN: 9781119750550 | Hardcover | 464 pages Price: $225 USD  |
One Line Description
The book is dedicated to the unique interdisciplinary research of the imagery processing, recognition and perception.
Audience
Of interest to a diverse audience, including engineers and scientists interested in the application of the concepts to people in the social sciences and extremely useful for students and researchers working in the field of imagery processing, recognition and perception
Of interest to a diverse audience, including engineers and scientists interested in the application of the concepts to people in the social sciences and extremely useful for students and researchers working in the field of imagery processing, recognition and perception
Description
This book is dedicated to the unique interdisciplinary research of imagery processing, recognition and perception. The contents of this book are based on the concepts of mathematical processing, compositional analysis applied in the art and design, and psychological factors of the information perception process.
The conduction of compositional analysis carried out in the course of images processing and recognition, creation of the image project solution and modeling of the conceptual space structures are considered together with the mechanism of their perception.
Edited and written by a group of international experts, the practical applications for industry are covered, including the influence of internet memes on social networks and face recognition technology subject to interferences. The algorithms of perception and improving of accuracy necessary for satellite imagery recognition and complex reflection from the object are represented with the use of artificial neural networks.
Not just a study in how humans recognize and perceive images, this outstanding new volume delves into how these processes are used in technology for continuously evolving industrial applications. Whether for the veteran scientist or engineer, or for the student, this is a must-have for any library.
Back to Top
This book is dedicated to the unique interdisciplinary research of imagery processing, recognition and perception. The contents of this book are based on the concepts of mathematical processing, compositional analysis applied in the art and design, and psychological factors of the information perception process.
The conduction of compositional analysis carried out in the course of images processing and recognition, creation of the image project solution and modeling of the conceptual space structures are considered together with the mechanism of their perception.
Edited and written by a group of international experts, the practical applications for industry are covered, including the influence of internet memes on social networks and face recognition technology subject to interferences. The algorithms of perception and improving of accuracy necessary for satellite imagery recognition and complex reflection from the object are represented with the use of artificial neural networks.
Not just a study in how humans recognize and perceive images, this outstanding new volume delves into how these processes are used in technology for continuously evolving industrial applications. Whether for the veteran scientist or engineer, or for the student, this is a must-have for any library.
Back to Top
Supplementary Data
• Combines mathematics, arts, and psychology to cover the basic concepts and applications for the recognition and perception of images
• Is based on mathematical processing, compositional analysis in art and design, and psychological factors of the information perception process
• Presents algorithms for increasing recognition accuracy using artificial neural networks of satellite images
• Presents the technologies of face recognition
• Combines mathematics, arts, and psychology to cover the basic concepts and applications for the recognition and perception of images
• Is based on mathematical processing, compositional analysis in art and design, and psychological factors of the information perception process
• Presents algorithms for increasing recognition accuracy using artificial neural networks of satellite images
• Presents the technologies of face recognition
Author / Editor Details
Iftikhar B. Abbasov, PhD, is a specialist in mathematical modeling, computer engineering and industrial design at the Southern Federal University in Russia. He has numerous publications to this credit, focusing on the use of mathematical modeling and high-level computer programming for practical applications such as ocean exploration, coastal and aircraft engineering, and perception of images.
Iftikhar B. Abbasov, PhD, is a specialist in mathematical modeling, computer engineering and industrial design at the Southern Federal University in Russia. He has numerous publications to this credit, focusing on the use of mathematical modeling and high-level computer programming for practical applications such as ocean exploration, coastal and aircraft engineering, and perception of images.
Back to Top
Table of Contents
Abstract xiii
Preface xv
1 Perception of Images. Modern Trends 1
Iftikhar B. Abbasov
1.1 Visual System 1
1.1.1 Some Modern Research 1
1.1.2 Light Perception 5
1.1.3 Vertebrate Eye Anatomy 5
1.1.4 Projection Areas of the Brain 8
1.2 Eye. Types of Eye Movement 10
1.2.1 Oculomotor Muscles and Field of View 10
1.2.2 Visual Acuity 10
1.2.3 Types of Eye Movement 12
1.2.4 Effects of Masking and Aftereffects 21
1.2.5 Perception of Contour and Contrast 23
1.2.6 Mach Bands, Hermann’s Grid 24
1.2.7 Light Contrast 27
1.2.8 Object Identification 27
1.2.9 Color Vision Abnormalities 28
1.3 Perception of Figures and Background 33
1.3.1 Dual Perception of the Connection
“Figure-Background” 34
1.3.2 Gestalt Grouping Factors 35
1.3.3 Subjective Contours 40
1.3.4 The Dependence of Perception on the Orientation
of the Figure 43
1.3.5 The Stroop Effect 45
1.4 Space Perception 46
1.4.1 Monocular Spatial Signs 46
1.4.2 Monocular Motion Parallax 48
1.4.3 Binocular Signs 48
1.4.4 Binocular Disparity and Stereopsis 49
1.5 Visual Illusions 49
1.5.1 Constancy Perception 51
1.5.2 The Development of the Process of Perception 52
1.5.3 Perception after Surgery Insight 53
1.5.4 Illusion of the Moon 54
1.5.5 Illusions of Muller-Lyer, Ponzo, Poggendorf, Zolner 55
1.5.6 Horizontal – Vertical Illusion 57
1.5.7 Illusions of Contrast 57
1.6 Conclusion 60
References 60
2 Image Recognition Based on Compositional Schemes 63
Victoria I. Barvenko and Natalia V. Krasnovskaya
2.1 Artistic Image 63
2.2 Classification of Features 69
2.3 Compositional Analysis of an Art Work 71
2.4 Classification by Shape, Position, Color 73
2.5 Classification According to the Content of the Scenes 76
2.6 Compositional Analysis in Iconography 80
2.7 Associative Mechanism of Analysis 83
2.8 Conclusions 86
References 86
3 Sensory and Project Images in the Design Practice 89
Anna A. Kuleshova
3.1 Sensory Image Nature 89
3.2 Language and Images Symbolics 96
3.3 Methods of Images Production in Ideas 102
3.4 Personality Image Projecting 106
3.5 Project Image 108
3.6 Conclusion 120
References 121
4 Associative Perception of Conceptual Models
of Exhibition Spaces 125
Olga P. Medvedeva
4.1 Associative Modeling of the Exhibition Space Environment 125
4.1.1 Introduction 125
4.1.2 Conceptual and Terminological Apparatus
of Conceptual Modeling and Shaping 127
4.1.3 Compositional and Planning Basis for Creating
the Environment of Exhibition Spaces 128
4.1.4 Scenario Approach in the Figurative Solution
of Environmental Spaces 128
4.1.5 Conceptual Approach to Creating Exhibition Spaces 129
4.1.6 Perception of the Figurative Solution
of the Environment 129
4.2 Associative Modeling of Environmental Objects
in Exhibition Spaces 134
4.2.1 Conceptual and Figurative Basis for the Formation
of Environmental Objects 134
4.2.2 Associative and Imaginative Modeling of the
Environmental Objects 134
4.2.3 Cognitive Bases of Perception of Associative-Figurative
Models of Objects in Environmental Spaces 135
4.2.4 Perception of the Figurative Solution of an
Environmental Object 136
4.2.5 Options of Conceptual and Figurative Modeling
of Objects in Environmental Spaces 136
4.3 Conclusion 141
References 141
5 Disentanglement For Discriminative Visual Recognition 143
Xiaofeng Liu
5.1 Introduction 144
5.2 Problem Statement. Deep Metric Learning Based
Disentanglement for FER 149
5.3 Adversarial Training Based Disentanglement 152
5.4 Methodology. Deep Metric Learning Based
Disentanglement for FER 154
5.5 Adversarial Training Based Disentanglement 159
5.5.1 The Structure of Representations 159
5.5.2 Framework Architecture 160
5.5.3 Informative to Main-Recognition Task 160
5.5.4 Eliminating Semantic Variations 161
5.5.5 Eliminating Latent Variation 162
5.5.6 Complementary Constraint 162
5.6 Experiments and Analysis 162
5.6.1 Deep Metric Learning Based Disentanglement for FER 162
5.6.2 Adversarial Training-Based Disentanglement 169
5.7 Discussion 176
5.7.1 Independent Analysis 176
5.7.2 Equilibrium Condition 176
5.8 Conclusion 178
References 179
6 Development of the Toolkit to Process the Internet Memes
Meant for the Modeling, Analysis, Monitoring and
Management of Social Processes 189
Margarita G. Kozlova, Vladimir A. Lukianenko
and Mariia S. Germanchuk
6.1 Introduction 190
6.2 Modeling of Internet Memes Distribution 193
6.3 Intellectualization of System for Processing the Internet
Meme Data Flow 197
6.4 Implementation of Intellectual System for Recognition
of Internet Meme Data Flow 207
6.5 Conclusion 216
References 217
7 The Use of the Mathematical Apparatus of Spatial Granulation
in The Problems of Perception and Image Recognition 221
Sergey A. Butenkov, Vitaly V. Krivsha and Nataly S. Krivsha
7.1 Introduction 221
7.2 The Image Processing and Analysis Base Conceptions 222
7.2.1 The Main Stages of Image Processing 222
7.2.2 The Fundamentals of a New Hybrid Approach
to Image Processing 223
7.2.3 How is this New Approach Different? 223
7.3 Human Visual Perception Modeling 224
7.3.1 Perceptual Classification of Digital Images 224
7.3.2 The Vague Models of Digital Images 226
7.4 Mathematic Modeling of Different Kinds of Digital Images 227
7.4.1 Images as the Special Kind of Spatial Data 228
7.4.2 Fundamentals of Topology and Digital Topology 230
7.4.3 Regularity and the Digital Topology
of Regular Regions 230
7.5 Zadeh’s Information Granulation Theory 232
7.6 Fundamentals of Spatial Granulation 235
7.6.1 Basic Ideas of Spatial Granulation 235
7.6.2 Abstract Vector Space 236
7.6.3 Abstract Affine Space 237
7.6.4 Cartesian Granules in an Affine Space 237
7.6.5 Granule-Based Measures in Affine Space 240
7.6.6 Fuzzy Spatial Relation Over the Granular Models 240
7.7 Entropy-Preserved Granulation of Spatial Data 241
7.8 Digital Images Granulation Algorithms 243
7.8.1 Matroids and Optimal Algorithms 244
7.8.2 Greedy Image Granulation Algorithms 244
7.9 Spatial Granulation Technique Applications 247
7.9.1 Granulation of Graphical DataBases 247
7.9.2 Automated Target Detection (ATD) Problem 250
7.9.3 Character Recognition Problem 251
7.9.4 Color Images Granulation in the Color Space 252
7.9.5 Spatial Granules Models for the Curvilinear Coordinates 253
7.9.6 Color Histogram for Color Images Segmentation 255
7.10 Conclusions 257
References 257
8 Inverse Synthetic Aperture Radars: Geometry, Signal Models
and Image Reconstruction Methods 261
Andon D. Lazarov and Chavdar N. Minchev
8.1 Introduction 261
8.2 ISAR Geometry and Coordinate Transformations 263
8.2.1 3-D Geometry of ISAR Scenario 263
8.2.2 3-D to 2-D ISAR Geometry Transformation 266
8.3 2-D ISAR Signal Models and Reconstruction Algorithms 274
8.3.1 Linear Frequency Modulation Waveform 274
8.3.2 2-D LFM ISAR Signal Model - Geometric
Interpretation of Signal Formation 275
8.3.3 ISAR Image Reconstruction Algorithm 277
8.3.4 Correlation - Spectral ISAR Image Reconstruction 279
8.3.5 Phase Correction Algorithm and Autofocusing 280
8.3.6 Barker Phase Code Modulation Waveform 289
8.3.7 Barker ISAR Image Reconstruction 290
8.3.8 Image Quality Criterion and Autofocusing 291
8.4 3-D ISAR Signal Models and Image Reconstruction
Algorithms 296
8.4.1 Stepped Frequency Modulated ISAR Signal Model 296
8.4.2 ISAR Image Reconstruction Algorithm 298
8.4.3 Complementary Codes and Phase Code Modulated
Pulse Waveforms 306
8.4.4 ISAR Complementary Phase Code Modulated
Signal Modeling 309
8.4.5 ISAR Image Reconstruction Procedure 311
8.4.6 Parametric ISAR Image Reconstruction 317
8.5 Conclusions 323
Acknowledgment 324
References 324
9 Remote Sensing Imagery Spatial Resolution Enhancement 327
Sergey A. Stankevich, Iryna O. Piestova and Mykola S. Lubskyi
9.1 Introduction 328
9.2 Multiband Aerospace Imagery Informativeness 328
9.3 Equivalent Spatial Resolution of Multiband
Aerospace Imagery 330
9.4 Multispectral Imagery Resolution Enhancement Based
on Spectral Signatures’ Identification 336
9.5 Multispectral Imagery Resolution Enhancement Using
Subpixels Values Reallocation According to Land Cover
Classes’ Topology 341
9.6 Remote Sensing Longwave Infrared Data Spatial
Resolution Enhancement 346
9.7 Issues of Objective Evaluation of Remote Sensing Imagery
Actual Spatial Resolution 359
9.8 Conclusion 360
References 361
10 The Theoretical and Technological Peculiarities of Aerospace
Imagery Processing and Interpretation By Means of Artificial
Neural Networks 369
Oleg G. Gvozdev
10.1 Introduction 371
10.2 Peculiarities of Aerospace Imagery, Ways of its Digital
Representation and Tasks Solved on it 373
10.2.1 Peculiarities of Technological Aerospace
Imaging Process 375
10.2.2 Aerospace Imagery Defects 378
10.2.3 Aerospace Imagery Channel/Spectral Structure 378
10.2.4 Aerospace Imagery Spatial Resolution 380
10.2.5 Radiometric Resolution of Aerospace Imagery 381
10.2.6 Aerospace Imagery Data Volumes 382
10.2.7 Aerospace Imagery Labeling 385
10.2.8 Limited Availability of Aerospace Imagery 386
10.2.9 Semantic Features of Aerospace Imagery 386
10.2.10 The Tasks Solved by Means of Aerospace Imagery 387
10.2.11 Conclusion 388
10.3 Aerospace Imagery Preprocessing 390
10.3.1 Technological Stack of Aerospace
Imagery Processing 391
10.3.2 Structuring and Accessing to Aerospace Datasets 392
10.3.3 Standardization of Measurements Representation 394
10.3.4 Handing of Random Channel/Spectral
Image Structure 397
10.3.5 Ensuring of Image Sizes Necessary for Processing 398
10.3.6 Tile-Based Image Processing 399
10.3.7 Design of Training Samples from the Aerospace
Imagery Sets 402
10.4 Interpretation of Aerospace Imagery by Means
of Artificial Neural Networks 406
10.4.1 ANN Topologies Building Framework Used
for Aerospace Imagery Processing 407
10.4.2 Object Non-Locality and Different Scales 413
10.4.3 Topology Customizing to the Different
Channel/Spectral Structures of Aerospace Imagery 418
10.4.4 Integration of Aerospace Imagery with
the Different Spatial Resolution 421
10.4.5 Instance Segmentation 421
10.4.6 Learning Rate Strategy 423
10.4.7 Program Interfaces Organization 424
10.4.8 Recommendations on the Framework Application 435
10.5 Conclusion 436
References 438
Index 445
Back to Top
Abstract xiii
Preface xv
1 Perception of Images. Modern Trends 1
Iftikhar B. Abbasov
1.1 Visual System 1
1.1.1 Some Modern Research 1
1.1.2 Light Perception 5
1.1.3 Vertebrate Eye Anatomy 5
1.1.4 Projection Areas of the Brain 8
1.2 Eye. Types of Eye Movement 10
1.2.1 Oculomotor Muscles and Field of View 10
1.2.2 Visual Acuity 10
1.2.3 Types of Eye Movement 12
1.2.4 Effects of Masking and Aftereffects 21
1.2.5 Perception of Contour and Contrast 23
1.2.6 Mach Bands, Hermann’s Grid 24
1.2.7 Light Contrast 27
1.2.8 Object Identification 27
1.2.9 Color Vision Abnormalities 28
1.3 Perception of Figures and Background 33
1.3.1 Dual Perception of the Connection
“Figure-Background” 34
1.3.2 Gestalt Grouping Factors 35
1.3.3 Subjective Contours 40
1.3.4 The Dependence of Perception on the Orientation
of the Figure 43
1.3.5 The Stroop Effect 45
1.4 Space Perception 46
1.4.1 Monocular Spatial Signs 46
1.4.2 Monocular Motion Parallax 48
1.4.3 Binocular Signs 48
1.4.4 Binocular Disparity and Stereopsis 49
1.5 Visual Illusions 49
1.5.1 Constancy Perception 51
1.5.2 The Development of the Process of Perception 52
1.5.3 Perception after Surgery Insight 53
1.5.4 Illusion of the Moon 54
1.5.5 Illusions of Muller-Lyer, Ponzo, Poggendorf, Zolner 55
1.5.6 Horizontal – Vertical Illusion 57
1.5.7 Illusions of Contrast 57
1.6 Conclusion 60
References 60
2 Image Recognition Based on Compositional Schemes 63
Victoria I. Barvenko and Natalia V. Krasnovskaya
2.1 Artistic Image 63
2.2 Classification of Features 69
2.3 Compositional Analysis of an Art Work 71
2.4 Classification by Shape, Position, Color 73
2.5 Classification According to the Content of the Scenes 76
2.6 Compositional Analysis in Iconography 80
2.7 Associative Mechanism of Analysis 83
2.8 Conclusions 86
References 86
3 Sensory and Project Images in the Design Practice 89
Anna A. Kuleshova
3.1 Sensory Image Nature 89
3.2 Language and Images Symbolics 96
3.3 Methods of Images Production in Ideas 102
3.4 Personality Image Projecting 106
3.5 Project Image 108
3.6 Conclusion 120
References 121
4 Associative Perception of Conceptual Models
of Exhibition Spaces 125
Olga P. Medvedeva
4.1 Associative Modeling of the Exhibition Space Environment 125
4.1.1 Introduction 125
4.1.2 Conceptual and Terminological Apparatus
of Conceptual Modeling and Shaping 127
4.1.3 Compositional and Planning Basis for Creating
the Environment of Exhibition Spaces 128
4.1.4 Scenario Approach in the Figurative Solution
of Environmental Spaces 128
4.1.5 Conceptual Approach to Creating Exhibition Spaces 129
4.1.6 Perception of the Figurative Solution
of the Environment 129
4.2 Associative Modeling of Environmental Objects
in Exhibition Spaces 134
4.2.1 Conceptual and Figurative Basis for the Formation
of Environmental Objects 134
4.2.2 Associative and Imaginative Modeling of the
Environmental Objects 134
4.2.3 Cognitive Bases of Perception of Associative-Figurative
Models of Objects in Environmental Spaces 135
4.2.4 Perception of the Figurative Solution of an
Environmental Object 136
4.2.5 Options of Conceptual and Figurative Modeling
of Objects in Environmental Spaces 136
4.3 Conclusion 141
References 141
5 Disentanglement For Discriminative Visual Recognition 143
Xiaofeng Liu
5.1 Introduction 144
5.2 Problem Statement. Deep Metric Learning Based
Disentanglement for FER 149
5.3 Adversarial Training Based Disentanglement 152
5.4 Methodology. Deep Metric Learning Based
Disentanglement for FER 154
5.5 Adversarial Training Based Disentanglement 159
5.5.1 The Structure of Representations 159
5.5.2 Framework Architecture 160
5.5.3 Informative to Main-Recognition Task 160
5.5.4 Eliminating Semantic Variations 161
5.5.5 Eliminating Latent Variation 162
5.5.6 Complementary Constraint 162
5.6 Experiments and Analysis 162
5.6.1 Deep Metric Learning Based Disentanglement for FER 162
5.6.2 Adversarial Training-Based Disentanglement 169
5.7 Discussion 176
5.7.1 Independent Analysis 176
5.7.2 Equilibrium Condition 176
5.8 Conclusion 178
References 179
6 Development of the Toolkit to Process the Internet Memes
Meant for the Modeling, Analysis, Monitoring and
Management of Social Processes 189
Margarita G. Kozlova, Vladimir A. Lukianenko
and Mariia S. Germanchuk
6.1 Introduction 190
6.2 Modeling of Internet Memes Distribution 193
6.3 Intellectualization of System for Processing the Internet
Meme Data Flow 197
6.4 Implementation of Intellectual System for Recognition
of Internet Meme Data Flow 207
6.5 Conclusion 216
References 217
7 The Use of the Mathematical Apparatus of Spatial Granulation
in The Problems of Perception and Image Recognition 221
Sergey A. Butenkov, Vitaly V. Krivsha and Nataly S. Krivsha
7.1 Introduction 221
7.2 The Image Processing and Analysis Base Conceptions 222
7.2.1 The Main Stages of Image Processing 222
7.2.2 The Fundamentals of a New Hybrid Approach
to Image Processing 223
7.2.3 How is this New Approach Different? 223
7.3 Human Visual Perception Modeling 224
7.3.1 Perceptual Classification of Digital Images 224
7.3.2 The Vague Models of Digital Images 226
7.4 Mathematic Modeling of Different Kinds of Digital Images 227
7.4.1 Images as the Special Kind of Spatial Data 228
7.4.2 Fundamentals of Topology and Digital Topology 230
7.4.3 Regularity and the Digital Topology
of Regular Regions 230
7.5 Zadeh’s Information Granulation Theory 232
7.6 Fundamentals of Spatial Granulation 235
7.6.1 Basic Ideas of Spatial Granulation 235
7.6.2 Abstract Vector Space 236
7.6.3 Abstract Affine Space 237
7.6.4 Cartesian Granules in an Affine Space 237
7.6.5 Granule-Based Measures in Affine Space 240
7.6.6 Fuzzy Spatial Relation Over the Granular Models 240
7.7 Entropy-Preserved Granulation of Spatial Data 241
7.8 Digital Images Granulation Algorithms 243
7.8.1 Matroids and Optimal Algorithms 244
7.8.2 Greedy Image Granulation Algorithms 244
7.9 Spatial Granulation Technique Applications 247
7.9.1 Granulation of Graphical DataBases 247
7.9.2 Automated Target Detection (ATD) Problem 250
7.9.3 Character Recognition Problem 251
7.9.4 Color Images Granulation in the Color Space 252
7.9.5 Spatial Granules Models for the Curvilinear Coordinates 253
7.9.6 Color Histogram for Color Images Segmentation 255
7.10 Conclusions 257
References 257
8 Inverse Synthetic Aperture Radars: Geometry, Signal Models
and Image Reconstruction Methods 261
Andon D. Lazarov and Chavdar N. Minchev
8.1 Introduction 261
8.2 ISAR Geometry and Coordinate Transformations 263
8.2.1 3-D Geometry of ISAR Scenario 263
8.2.2 3-D to 2-D ISAR Geometry Transformation 266
8.3 2-D ISAR Signal Models and Reconstruction Algorithms 274
8.3.1 Linear Frequency Modulation Waveform 274
8.3.2 2-D LFM ISAR Signal Model - Geometric
Interpretation of Signal Formation 275
8.3.3 ISAR Image Reconstruction Algorithm 277
8.3.4 Correlation - Spectral ISAR Image Reconstruction 279
8.3.5 Phase Correction Algorithm and Autofocusing 280
8.3.6 Barker Phase Code Modulation Waveform 289
8.3.7 Barker ISAR Image Reconstruction 290
8.3.8 Image Quality Criterion and Autofocusing 291
8.4 3-D ISAR Signal Models and Image Reconstruction
Algorithms 296
8.4.1 Stepped Frequency Modulated ISAR Signal Model 296
8.4.2 ISAR Image Reconstruction Algorithm 298
8.4.3 Complementary Codes and Phase Code Modulated
Pulse Waveforms 306
8.4.4 ISAR Complementary Phase Code Modulated
Signal Modeling 309
8.4.5 ISAR Image Reconstruction Procedure 311
8.4.6 Parametric ISAR Image Reconstruction 317
8.5 Conclusions 323
Acknowledgment 324
References 324
9 Remote Sensing Imagery Spatial Resolution Enhancement 327
Sergey A. Stankevich, Iryna O. Piestova and Mykola S. Lubskyi
9.1 Introduction 328
9.2 Multiband Aerospace Imagery Informativeness 328
9.3 Equivalent Spatial Resolution of Multiband
Aerospace Imagery 330
9.4 Multispectral Imagery Resolution Enhancement Based
on Spectral Signatures’ Identification 336
9.5 Multispectral Imagery Resolution Enhancement Using
Subpixels Values Reallocation According to Land Cover
Classes’ Topology 341
9.6 Remote Sensing Longwave Infrared Data Spatial
Resolution Enhancement 346
9.7 Issues of Objective Evaluation of Remote Sensing Imagery
Actual Spatial Resolution 359
9.8 Conclusion 360
References 361
10 The Theoretical and Technological Peculiarities of Aerospace
Imagery Processing and Interpretation By Means of Artificial
Neural Networks 369
Oleg G. Gvozdev
10.1 Introduction 371
10.2 Peculiarities of Aerospace Imagery, Ways of its Digital
Representation and Tasks Solved on it 373
10.2.1 Peculiarities of Technological Aerospace
Imaging Process 375
10.2.2 Aerospace Imagery Defects 378
10.2.3 Aerospace Imagery Channel/Spectral Structure 378
10.2.4 Aerospace Imagery Spatial Resolution 380
10.2.5 Radiometric Resolution of Aerospace Imagery 381
10.2.6 Aerospace Imagery Data Volumes 382
10.2.7 Aerospace Imagery Labeling 385
10.2.8 Limited Availability of Aerospace Imagery 386
10.2.9 Semantic Features of Aerospace Imagery 386
10.2.10 The Tasks Solved by Means of Aerospace Imagery 387
10.2.11 Conclusion 388
10.3 Aerospace Imagery Preprocessing 390
10.3.1 Technological Stack of Aerospace
Imagery Processing 391
10.3.2 Structuring and Accessing to Aerospace Datasets 392
10.3.3 Standardization of Measurements Representation 394
10.3.4 Handing of Random Channel/Spectral
Image Structure 397
10.3.5 Ensuring of Image Sizes Necessary for Processing 398
10.3.6 Tile-Based Image Processing 399
10.3.7 Design of Training Samples from the Aerospace
Imagery Sets 402
10.4 Interpretation of Aerospace Imagery by Means
of Artificial Neural Networks 406
10.4.1 ANN Topologies Building Framework Used
for Aerospace Imagery Processing 407
10.4.2 Object Non-Locality and Different Scales 413
10.4.3 Topology Customizing to the Different
Channel/Spectral Structures of Aerospace Imagery 418
10.4.4 Integration of Aerospace Imagery with
the Different Spatial Resolution 421
10.4.5 Instance Segmentation 421
10.4.6 Learning Rate Strategy 423
10.4.7 Program Interfaces Organization 424
10.4.8 Recommendations on the Framework Application 435
10.5 Conclusion 436
References 438
Index 445
Back to Top
BISAC SUBJECT HEADINGS
COM016000: COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition
SCI090000 : SCIENCE / Cognitive Science
TEC030000 : TECHNOLOGY & ENGINEERING / Optics
BIC CODES
UYZ: Human-computer interaction
UYQV: Computer vision
AJR: Special kinds of photography
Back to Top