A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions
-
Amit Sharma
,
Sarita Naruka
and
Gowri Choudhary
Abstract
Microwave remote sensingmicrowave remote sensing has emerged as a vital tool for Earth observation due to its ability to operate in all-weather and day-or-night conditions, which are significant limitations of optical sensors. This chapter systematically reviews deep learning (DL) techniques for microwave remote sensingmicrowave remote sensing to enhance data interpretation and application accuracy. The chapter highlights the effectiveness of Siamese networks and autoencodersautoencoders in change detection applications. Challenges such as the scarcity of labeled data, computational costs, and model interpretability are discussed, along with potential solutions such as transfer learning and federated learning. The review also emphasizes the role of synthetic aperture radar (SARSAR) data in capturing spatial and temporal features. By exploring the strengths and limitations of these methods, the chapter provides insights into the future scope of DL applications in microwave remote sensing, aiming to effectively support environmental monitoringenvironmental monitoring, disaster managementdisaster management, and sustainable development initiatives.
Abstract
Microwave remote sensingmicrowave remote sensing has emerged as a vital tool for Earth observation due to its ability to operate in all-weather and day-or-night conditions, which are significant limitations of optical sensors. This chapter systematically reviews deep learning (DL) techniques for microwave remote sensingmicrowave remote sensing to enhance data interpretation and application accuracy. The chapter highlights the effectiveness of Siamese networks and autoencodersautoencoders in change detection applications. Challenges such as the scarcity of labeled data, computational costs, and model interpretability are discussed, along with potential solutions such as transfer learning and federated learning. The review also emphasizes the role of synthetic aperture radar (SARSAR) data in capturing spatial and temporal features. By exploring the strengths and limitations of these methods, the chapter provides insights into the future scope of DL applications in microwave remote sensing, aiming to effectively support environmental monitoringenvironmental monitoring, disaster managementdisaster management, and sustainable development initiatives.
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
