Essentials of RADAR remote sensing and AI integration
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Syna
Abstract
This chapter provides a foundational understanding of the integration between radio detection and ranging (RADAR) remote sensingremote sensing and artificial intelligence (AI). It begins with fundamental RADAR concepts, including system classifications, data acquisition techniques, and signal processing. The chapter explores the role of AI in processing and interpreting remote sensing data, discussing various machine learning and deep learning approaches used to enhance RADAR-based analytics. This chapter evaluates AI’s benefits and limitations in RADAR applications, focusing on computational efficiency, data accuracy, and operational challenges. The chapter discusses key applications in environmental monitoringenvironmental monitoring, including deforestation assessment, flood detectionflood detection, urban mapping, and agricultural advancements such as precision farming and pest control. By outlining the synergy between RADAR and AI, this chapter outlines how these technologies contribute to more efficient and accurate remote sensing solutions.
Abstract
This chapter provides a foundational understanding of the integration between radio detection and ranging (RADAR) remote sensingremote sensing and artificial intelligence (AI). It begins with fundamental RADAR concepts, including system classifications, data acquisition techniques, and signal processing. The chapter explores the role of AI in processing and interpreting remote sensing data, discussing various machine learning and deep learning approaches used to enhance RADAR-based analytics. This chapter evaluates AI’s benefits and limitations in RADAR applications, focusing on computational efficiency, data accuracy, and operational challenges. The chapter discusses key applications in environmental monitoringenvironmental monitoring, including deforestation assessment, flood detectionflood detection, urban mapping, and agricultural advancements such as precision farming and pest control. By outlining the synergy between RADAR and AI, this chapter outlines how these technologies contribute to more efficient and accurate remote sensing solutions.
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
