Machine Learning Based Air Traffic Surveillance System Using Image Processing
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- Contents
- About
Machine Learning Based Air Traffic Surveillance System Using Image Processing analyses how advanced machine learning algorithms and image processing technologies are revolutionising air-traffic management. By integrating real-time visual data analysis with sophisticated artificial intelligence techniques, this book highlights the potential to enhance situational awareness, safety, and efficiency in managing increasingly complex and congested airspaces. It delves into the use of convolutional neural networks (CNNs) and deep learning models to identify, track, and analyse aircraft movements, offering precise and actionable insights for air-traffic controllers.
This comprehensive resource combines theoretical foundations with practical applications, including real-world case studies and discussions on system implementation. It addresses critical aspects such as object detection, anomaly identification, and trajectory prediction, alongside regulatory, ethical, and cybersecurity considerations. With its blend of cutting-edge research and practical insights, this book is an invaluable guide for professionals, researchers, and students in aerospace engineering, artificial intelligence, and computer vision, providing a roadmap for advancing air-traffic surveillance and management in the era of intelligent systems.
Chapter 1. Advanced Image Processing Techniques for Smart Air Traffic Monitoring; Hridoy Das
- Chapter 2. Explainable AI (XAI) in Air Traffic Monitoring Systems; Madeha Memon, Sanam Narejo, Shahnawaz Talpur, Asma Channa, Fawad Ali Mangi, and Jay Kumar Pandey
- Chapter 3. Machine Learning and Image Processing Integration Air Traffic; Ankur Mittal, Mahesh K. Singh, and Nitin Singh Singha
- Chapter 4. Image Processing Techniques in Sovan Air Traffic Monitoring; Smaranika Roy, Piyal Roy, and Rajat Pandit
- Chapter 5. AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance; Fredrick Kayusi, Petros Chavula, Linety Juma, Rashmi Mishra, Maad M. Mijwil, and Mostafa Abotaleb
- Chapter 6. Advanced AI-Enabled UAV Swarms for Real-time Air Traffic Surveillance; Mahesh K. Singh, Nitin Singh Singha, and Vidit Datt Prabhakar
- Chapter 7. A Robust Intelligent Framework for Air Traffic Management System Using Machine Learning; Bremananth R and Awashreh R
- Chapter 8. Factoring Explainability and Transparency in Machine Learning-Based Air Traffic Surveillance; Wasswa Shafik
- Chapter 9. Enhancing Air Traffic Surveillance with Machine Learning; R. Anita, C. Pretty Diana Cyril, and J. Briskilal
- Chapter 10. AI-Powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN-EGSA Optimization Techniques; Saisuman Singamsetty
- Chapter 11. Optimization of Airspace using Pigeon Feather Flight Path Optimisation (PFO) Algorithm in India; Saifullah Khalid
- Chapter 12. Enhancing IoT Surveillance Systems Using DL and Big Data for Advanced Security Protocols; Ankur Gupta and Dinesh Chandra Misra
- Chapter 13. Leveraging AI and IoT for Advanced Air Traffic Surveillance and Collision Avoidance; Sheeja Pon Chakravarthy, R. Pavithra, and Anu Prabhakar
- Chapter 14. Exploring the Use of AI in the Aviation Sector: A Comprehensive Bibliographic Evaluation; Saurabh Mitra and Sanjeev Kumar Gupta
Jay Kumar Pandey is an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, India.
Mritunjay Rai is an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, India.
Faizan Ahmad is a Lecturer in Computer Science and/or Games Development at Cardiff School of Technologies, Cardiff Metropolitan University, UK.