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.
Multisensor Remote Sensing Data Fusion for Enhanced Earth Observation
Publisher: Wiley-Scrivener
Editors: Neelam Dahiya, Narayan Vyas, Sartajvir Singh, Ankit Tyagi
This book, Multisensor Remote Sensing Data Fusion for Enhanced Earth Observation, presents a comprehensive exploration of the principles, methodologies, and applications of integrating data from multiple remote sensing sensors. As Earth observation evolves, the demand for accurate, high-resolution, and timely geospatial information has led to the increasing use of data fusion techniques that combine complementary strengths of diverse sensors such as optical, microwave, LiDAR, and hyperspectral systems. The book begins with foundational concepts in remote sensing and sensor fusion, explaining key fusion levels—pixel, feature, and decision—and the characteristics of various satellite platforms. It also addresses essential preprocessing steps such as radiometric, geometric, and temporal alignment for harmonizing heterogeneous datasets.
| Important Dates | |
|---|---|
| Abstract Submission Deadline | 30 July 2025 |
| Abstract Acceptance Notification | 30 August 2025 |
| Full Chapter Submission Deadline | 30 September 2025 |
| Chapter Acceptance Notification | 30 November 2025 |
| Projected Book Release Date | October 2026 |
| Important Guidelines | |
|---|---|
| Citation Style | IEEE |
| Formatting | 11 pt Times Roman, 1.5 line spacing |
| Originality | Plagiarism Under 10%, 0% AI Generated Content |
| Headings |
|
Chapter 1: Fundamentals of Multisensor Remote Sensing and Earth Observation
Chapter 2: Core Principles of Sensor Fusion: Pixel-Level, Feature-Level, and Decision-Level Approaches
Chapter 3: Remote Sensing Modalities and Platforms: Optical, Microwave, and Hyperspectral Sensors
Chapter 4: Preprocessing Techniques for Multisensor Geospatial Data Fusion
Chapter 5: Comparative Analysis of Single-Sensor and Multisensor Data Fusion Approaches
Chapter 6: Fusion of Optical and Microwave Data: Case Studies Using Sentinel-1 and Sentinel-2
Chapter 7: Hyperspectral–Multispectral Data Fusion for Enhanced Land Cover Classification
Chapter 8: SAR–Optical Synergy for Land Surface Mapping: Principles, Methods, and Applications
Chapter 9: Machine Learning Techniques for Multisensor Fusion: Applications in Land Cover Classification and Change Detection
Chapter 10: Deep Learning Techniques in Multisensor Fusion: Applications in Feature Extraction and Environmental Monitoring
Chapter 11: Agricultural Land Classification and Crop Monitoring Using Multisensor Remote Sensing Data
Chapter 12: Multisensor Fusion in Cryosphere: Monitoring Snow Cover, Glacial Extent, and Permafrost
Chapter 13: Urban Growth Analysis and Infrastructure Mapping Through Fusion-Based Approaches Using Google Earth Engine
Chapter 14: Multisensor Data-Based Land Use and Land Cover Classification and Change Detection
Chapter 15: Future Perspectives and Emerging Trends in Multisensor Data Fusion for Remote Sensing
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