Contents

Chapters in this book

1. Frontmatter I
2. Preface V
3. Contents VII
4. Integrating Sentinel-1 satellite data with machine learning for land use classification 1
5. A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
6. Fundamentals of active and passive microwave remote sensing: principles and applications 31
7. Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
8. Essentials of RADAR remote sensing and AI integration 73
9. Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
10. AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
11. Fusion of the optical and microwave images for cloud removal 123
12. Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
13. Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
14. Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
15. Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
16. Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
17. Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
18. Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
19. Editors’ biographies
20. Index 247