Chapter 4: Image Processing Techniques in Sovan Air Traffic Monitoring Available to Purchase

Due to the increasing complexity of air traffic control (ATC), new ways must be introduced to ensure security, efficiency, and flexibility. Image processing is, without a doubt, one of the most important technologies to deal with these problems in complex systems such as Sovan Air Traffic Monitoring. Basic image processing such as feature extraction, edge recognition, and noise reduction are applied to the problem of traffic monitoring situations, and the relevance of the methods is investigated. Advanced methods such as feature-based classification, picture segmentation, and object recognition are encouraged to identify, track, and classify airplanes under different effects of environments. Image processing should be made more precise and resilient to convolutional neural networks (CNNs) and other machine learning (ML) models. The chapter sees some real-world applications, such as traffic flow monitoring, collision detection, and automated decision-making, among others. Care is taken regarding moral quandaries, computing limits, and lighting shifts. Providing a comprehensive resource for the researchers and practitioners who will use image processing in today's and tomorrow's ATC, this chapter presents a meta viewer, which allows researchers to view all history with a single click, and an interactive ATC system.