Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change
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Hardik Dhiman
Abstract
This chapter delves into advanced RADAR remote sensingRADAR remote sensing applications powered by artificial intelligence (AI). As AI techniques advance, RADAR dataRADAR data is increasingly utilized to address complex global challenges such as disaster response, military intelligence, and climate change adaptation. The chapter discusses advanced AI methodologies that enhance RADAR’s predictive capabilities in detecting earthquakes, landslideslandslides, and floods, as well as monitoring and managing wildfireswildfires. Additionally, the chapter explores the integration of RADAR with other remote-sensing technologies to improve situational awareness in military operations and environmental conservation efforts. It also examines challenges such as data fusion, large-scale RADAR processing, and the demand for computational efficiency. The chapter highlights emerging AI-driven innovations that are set to revolutionize RADAR applications in remote sensing, paving the way for more precise and autonomous decision-making systems.
Abstract
This chapter delves into advanced RADAR remote sensingRADAR remote sensing applications powered by artificial intelligence (AI). As AI techniques advance, RADAR dataRADAR data is increasingly utilized to address complex global challenges such as disaster response, military intelligence, and climate change adaptation. The chapter discusses advanced AI methodologies that enhance RADAR’s predictive capabilities in detecting earthquakes, landslideslandslides, and floods, as well as monitoring and managing wildfireswildfires. Additionally, the chapter explores the integration of RADAR with other remote-sensing technologies to improve situational awareness in military operations and environmental conservation efforts. It also examines challenges such as data fusion, large-scale RADAR processing, and the demand for computational efficiency. The chapter highlights emerging AI-driven innovations that are set to revolutionize RADAR applications in remote sensing, paving the way for more precise and autonomous decision-making systems.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
