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.
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.
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.
