Fundamentals of active and passive microwave remote sensing: principles and applications
-
Yash Mahajan
,
Ajay Pal Singh
Abstract
Microwave remote sensing is widely utilized across various disciplines for Earth observation. This chapter explores both the fundamental principles and practical applications of microwave remote sensing, with a focus on the distinctions between active and passive approaches. Active microwave remote sensing, particularly radar systems, provides high-resolution, independent data that is essential for applications such as disaster monitoring, environmental mapping, and infrastructure assessment. However, its significance is comparatively lower in studies related to atmospheric analysis, oceanography, and climate monitoring, where passive microwave remote sensing is more relevant. Additionally, this chapter discusses key concepts, including the interaction of microwave radiation with the Earth’s surface, and highlights the growing role of artificial intelligence in enhancing data analysis and interpretation. Furthermore, it examines the integration of microwave remote sensing with contemporary applications, particularly in addressing environmental challenges, societal issues, and technological advancements.
Abstract
Microwave remote sensing is widely utilized across various disciplines for Earth observation. This chapter explores both the fundamental principles and practical applications of microwave remote sensing, with a focus on the distinctions between active and passive approaches. Active microwave remote sensing, particularly radar systems, provides high-resolution, independent data that is essential for applications such as disaster monitoring, environmental mapping, and infrastructure assessment. However, its significance is comparatively lower in studies related to atmospheric analysis, oceanography, and climate monitoring, where passive microwave remote sensing is more relevant. Additionally, this chapter discusses key concepts, including the interaction of microwave radiation with the Earth’s surface, and highlights the growing role of artificial intelligence in enhancing data analysis and interpretation. Furthermore, it examines the integration of microwave remote sensing with contemporary applications, particularly in addressing environmental challenges, societal issues, and technological advancements.
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
