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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.

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RADAR: Remote Sensing Data Analysis with Artificial Intelligence

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 Deadline30 August 2024
Abstract Acceptance Notification30 September 2024
Full Chapter Submission Deadline30 October 2024
Chapter Acceptance Notification30 November 2024
Projected Book Release DateJune 2025
Important Guidelines
Citation StyleHARVARD
OriginalityPlagiarism Under 10%, 0% AI Generated Content
Text Style11 pt Times New Roman, 1.5 line spacing
Headings3 numbered headings (e.g. 1, 1.1, 1.1.1), one unnumbered heading
FiguresHigh-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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