Narayan Vyas, an academician at Vivekananda Global University, specializes in computer science, focusing on IoT and Mobile App Development. He has trained over 1000 students globally and published extensively in Scopus journals. An active IEEE member, his research interests include Remote Sensing, Machine Learning, and Computer Vision.
RADAR: Remote Sensing Data Analysis with Artificial Intelligence
Publisher: De Gruyter
Editors: Alessandro Vinciarelli, Sartajvir Singh, Narayan Vyas, Mona Abdelbaset Sadek Ali
This book explores the integration of RADAR remote sensing with AI techniques. It covers the evolution of RADAR missions, fundamentals of microwave remote sensing, and AI-enhanced applications. Topics include big data analysis, machine learning algorithms, and the fusion of SAR and optical remote sensing for better classification and change detection. Emerging tools like IoT and deep learning for SAR image interpretation are highlighted, along with AI-enabled RADAR applications in urban monitoring, agriculture, and disaster management, emphasizing future trends and impacts.
| Important Dates | |
|---|---|
| Abstract Submission Deadline | 10 December 2024 (Revised) |
| Abstract Acceptance Notification | 12 December 2024 |
| Full Chapter Submission Deadline | 17 December 2024 (Revised) |
| Chapter Acceptance Notification | 30 January 2024 |
| Projected Book Release Date | August 2025 |
| Important Guidelines | |
|---|---|
| Citation Style | HARVARD |
| Originality | Plagiarism Under 10%, 0% AI Generated Content |
| Text Style | 11 pt Times New Roman, 1.5 line spacing |
| Headings | 3 numbered headings (e.g. 1, 1.1, 1.1.1), one unnumbered heading |
| Figures | High-quality, original figures, 300 dpi |
Chapter 1: Introduction to RADAR Remote Sensing and AI
Chapter 2: Evolution of various RADAR missions and sensors
Chapter 3: Fundamentals of active and passive microwave remote sensing
Chapter 4: Potential applications of RADAR remote sensing with AI
Chapter 5: Big data analysis of RADAR remote sensing: Challenges and Solutions
Chapter 6: Advanced Machine/Deep Learning algorithms for RADAR remote sensing
Chapter 7: Fusion of SAR/Scatterometer and Optical Remote Sensing: Enhanced Classification and Change Detection
Chapter 8: Role of emerging tool and technologies like IoT in enhancing RADAR capabilities
Chapter 9: Deep Learning in SAR: Enhancing Image Interpretation
Chapter 10: Emerging Trends in AI for RADAR remote sensing
Chapter 11: Urban Infrastructure Monitoring with AI and RADAR-based Remote Sensing
Chapter 12: Agricultural Insights: SAR for Crop Yield Estimation and Soil Monitoring
Chapter 13: Disaster Management: Leveraging SAR for Environmental Sustainability
Chapter 14: Emerging AI-enabled RADAR Applications in real-time scenarios
Chapter 15: Future Scope of RADAR Remote Sensing with AI
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