Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects
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Gowda V. Dankan
Abstract
The integration of artificial intelligence (AI) in RADAR remote sensingRADAR remote sensing is revolutionizing data analysis, feature extractionfeature extraction, and object identification. Machine learning and deep learning models, including convolutional neural networks and recurrent neural networks, significantly enhance RADAR imageryRADAR imagery interpretation by improving object detection, terrain classification, and noise reduction. Synthetic aperture radar (SAR), interferometric SAR (InSARInSAR)interferometric SAR (InSAR), and polarimetric RADARpolarimetric RADAR benefit from AI-driven solutions that enable real-time processing, improving environmental monitoring, defense applications, and disaster response. Advanced AI techniques, such as explainable AI and generative adversarial networks, optimize feature extraction while addressing challenges like data noise, high computational costs, and low-resolution imagery. AI-driven RADAR systemsRADAR systems are crucial for automation in self-driving vehicles, precision agriculture, and military surveillance. This chapter explores these advancements, discussing future prospects in AI-driven RADAR sensing, including cloud computing integration and enhanced computational efficiency for real-time, large-scale applications.
Abstract
The integration of artificial intelligence (AI) in RADAR remote sensingRADAR remote sensing is revolutionizing data analysis, feature extractionfeature extraction, and object identification. Machine learning and deep learning models, including convolutional neural networks and recurrent neural networks, significantly enhance RADAR imageryRADAR imagery interpretation by improving object detection, terrain classification, and noise reduction. Synthetic aperture radar (SAR), interferometric SAR (InSARInSAR)interferometric SAR (InSAR), and polarimetric RADARpolarimetric RADAR benefit from AI-driven solutions that enable real-time processing, improving environmental monitoring, defense applications, and disaster response. Advanced AI techniques, such as explainable AI and generative adversarial networks, optimize feature extraction while addressing challenges like data noise, high computational costs, and low-resolution imagery. AI-driven RADAR systemsRADAR systems are crucial for automation in self-driving vehicles, precision agriculture, and military surveillance. This chapter explores these advancements, discussing future prospects in AI-driven RADAR sensing, including cloud computing integration and enhanced computational efficiency for real-time, large-scale applications.
Chapters in this book
- 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
Chapters in this book
- 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
