Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making

Dankan G.V.; Prasad V.N.; Quevedo Lezama C.R.; Srinivas D.; Lakineni P.K. · RADAR: Remote Sensing Data Analysis with Artificial Intelligence (2025) · DOI: 10.1515/9783111572970-009

Resumen

This chapter focuses on using artificial intelligence (AI) in RADAR remote sensing, with applications such as enhanced target detection and noise reduction. Various advanced techniques in the field of AI, including convolutional neural networks (CNNs) and recurrent neural networks, have improved the efficiency of RADAR systems. Autoencoders and generative adversarial networks can be used to enhance the quality of images. These techniques also accelerate real-time data processing, improving RADAR system responsiveness in disaster monitoring and autonomous navigation. This chapter addresses these challenges, explores methods to enhance AI models for RADAR applications, and outlines future research directions. Integrating AI into RADAR systems enhances their intelligence, autonomy, and ability to address climate monitoring, defense, and smart city application challenges.

Instituciones: Universidad Nacional Mayor de San Marcos