Preface......Page 6
Contents......Page 8
1.1 Introduction......Page 10
1.1.1 History of AI......Page 12
1.2 Stages and Processes of Artificial Intelligence......Page 13
1.3 Practical Application and Future of AI......Page 14
1.4 Augmented Reality......Page 15
1.4.1 History of Augmented Reality......Page 17
1.5 Substantive AR Hardware Components......Page 18
1.5.3 Displays......Page 19
1.5.4 Substantive AR Software Components......Page 20
1.5.5 Mobile Augmented Reality......Page 21
1.5.6.1 Optical Tracking......Page 23
1.5.6.2 Types of Optical Tracking Methods......Page 24
1.5.8 Interaction......Page 25
1.5.8.3 Convergence of Satellite Image, Augmented Reality, and Artificial Intelligence......Page 26
1.6.1 Damage Detection......Page 28
1.6.3 Land Area Usage System......Page 30
1.7 Conclusion and Future Scope......Page 31
References......Page 32
2 Multithreading Approach for Clustering of Multiplane Satellite Images......Page 34
2.1 Introduction......Page 35
2.2 Literature Survey......Page 36
2.3 Parallel Computation......Page 37
2.3.1 K-Means Clustering of Multispectral Images......Page 39
2.4 Experimental Results and Discussions......Page 41
2.4.1 Implementation......Page 42
2.4.2 Performance Evaluation......Page 46
References......Page 55
3 Classification of Field-Level Crop Types with a Time Series Satellite Data Using Deep Neural Network......Page 57
3.1 Introduction......Page 58
3.2.2 Data......Page 59
3.2.3 Support Vector Machine......Page 60
3.2.4 DNN-Based Classification Model......Page 61
3.2.5 Implementation......Page 63
3.3 Result Analysis......Page 64
3.3.1 Time Series Profile for the Classified Data......Page 72
3.4 Conclusion......Page 73
References......Page 74
4.1 Introduction......Page 76
4.2 Motivations and Problem Statement......Page 79
4.3 Related Works......Page 80
4.4 Methodology......Page 82
4.4.2 Convolutional Neural Network......Page 83
4.4.3 Results and Discussion......Page 85
References......Page 87
5 Artificial Bee Colony-Optimized Contrast Enhancement for Satellite Image Fusion......Page 90
5.1 Introduction......Page 91
5.2 Materials and Methods......Page 94
5.2.2 Contrast Enhancement of Satellite Image......Page 95
5.2.2.1 Global Histogram Equalization......Page 96
5.2.2.2 Gamma Correction......Page 97
5.2.2.3 Artificial Bee Colony Optimization-Based Modeling......Page 98
5.2.3.1 PCA-Based Image Fusion......Page 101
5.2.3.2 DWT-Based Image Fusion......Page 102
5.3 Results and Discussions......Page 104
5.4 Conclusion......Page 109
References......Page 111
6 Effective Transform Domain Denoising of Oceanographic SAR Images for Improved Target Characterization......Page 113
6.1 Introduction......Page 114
6.2 Proposed Approach......Page 115
6.3.1 Median Filter......Page 116
6.3.3 Kuan Filter......Page 117
6.3.5 Adaptive Mean Filter......Page 118
6.3.7 Anisotropic Diffusion......Page 119
6.4.1 Despeckling Using Wavelet Transform......Page 120
6.4.3 Despeckling Using Curvelet Transform......Page 122
6.6 Performance Measure......Page 123
6.7.2 Denoising Results Using Multiresolution Transforms......Page 125
6.7.3 Denoising Results Using Hybrid Approach......Page 135
6.9 Conclusion......Page 141
References......Page 142
7.1 Introduction......Page 143
7.2.1 Image......Page 146
7.2.1.1 Digital Image......Page 147
7.2.2 Emergence of Fuzzy Sets in Digital Image......Page 148
7.2.2.2 Intuitionistic Fuzzy Set of an Image......Page 149
7.2.2.3 Interval-Valued Intuitionistic Fuzzy Set of an Image......Page 150
7.2.3.1 Image Fusion......Page 151
7.2.4 Identification of Disease Infected Crop SAR Images......Page 153
7.3.1 Fusion of SAR Images......Page 155
7.3.2 Proposed Interval-Valued Intuitionistic Fuzzy C-Means Clustering Technique (IVIFCM) Segmentation Technique......Page 156
7.4.1 Accuracy......Page 157
7.4.6 SSIM......Page 158
7.4.7 Results and Discussion......Page 159
7.5 Conclusion......Page 163
References......Page 164
8.1 Introduction......Page 166
8.1.2 Image Semantic Segmentation......Page 167
8.1.3 Deep Learning Over Machine Learning......Page 168
8.2.1 Back Propagation......Page 169
8.3.1 Mini-Batch Gradient Descent Hyperparameters......Page 171
8.3.3 Loss Function......Page 172
8.4.1 Convolutional Layer......Page 173
8.4.3 Fully Connected Layer......Page 175
8.5.1 FCN (Fully Convlutional Neural Network)......Page 176
8.5.4 Mask R-CNN......Page 177
8.7.1 Satellite Images Dataset......Page 178
8.7.2 Preprocessing and Mask Generation......Page 179
8.8.2 Training the Model......Page 182
8.9.1 Preprocessing Images for Network Training......Page 183
8.9.3 Loss Function......Page 184
8.10.1 Software and Processor for Model Training......Page 185
References......Page 192
9 Change Detection of Tropical Mangrove Ecosystem with Subpixel Classification of Time Series HyperspectralImagery......Page 194
9.1 Introduction......Page 195
9.2 Study Area......Page 197
9.3 Methodology......Page 198
9.3.1 Data Acquisition......Page 199
9.3.2 Ground Survey......Page 200
9.4 Calculation of Normalized Differential Vegetation Index (NDVI)......Page 202
9.6 Least Square Error-Based Spectral Unmixing......Page 203
9.7 Experimental Results and Discussions......Page 204
9.8 Conclusion......Page 211
References......Page 215
10 Crop Classification and Mapping for Agricultural Land from Satellite Images......Page 217
10.1 Introduction......Page 218
10.2 Land Utilization and Crop Pattern in Tamil Nadu......Page 220
10.3 Literature Survey......Page 221
10.4.1 Minimum Distance from Mean (MDM) Classification......Page 227
10.4.4 K-Means Classification......Page 228
10.4.7 Artificial Neural Networks (ANN)......Page 229
10.4.9 Decision Rule-Based Tree Classification......Page 230
10.5 System Methodology......Page 231
10.6 Results and Discussion......Page 232
References......Page 236
11.1 Introduction......Page 238
11.2 Machine Learning in Satellite Data Processing......Page 240
11.2.1 Satellite Image Classification......Page 242
11.2.2 Kernel Base Extraction......Page 243
11.3.1 Manifold Learning......Page 244
11.3.3 Transfer Learning......Page 245
11.3.5 Structured Learning......Page 246
11.4.2 Recurrent Neural Network......Page 247
11.4.4 Deep Belief Network......Page 248
11.5 Case Studies......Page 249
11.6 Conclusion......Page 252
References......Page 254
12.1 Introduction......Page 258
12.2 Wavelet-Based Water Extraction Method......Page 261
12.3 Experimental Results and Discussion......Page 266
12.3.1 Performance Evaluation......Page 273
References......Page 275