Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making
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Dankan Gowda V
Abstract
This chapter focuses on using artificial intelligence (AI)artificial intelligence (AI) in RADAR remote sensing, with applications such as enhanced target detectiontarget detection and noise reduction. Various advanced techniques in the field of AI, including convolutional neural networks (CNNsCNNs) and recurrent neural networks, have improved the efficiency of RADAR systemsRADAR systems. Autoencoders and generative adversarial networks can be used to enhance the quality of images. These techniques also accelerate real-time datareal-time data processing, improving RADAR system responsiveness in disaster monitoringdisaster monitoring and autonomous navigation. This chapter addresses these challenges, explores methods to enhance AI models for RADAR applications, and outlines future research directions. Integrating AI into RADAR systems enhances their intelligence, autonomy, and ability to address climate monitoring, defense, and smart city application challenges.
Abstract
This chapter focuses on using artificial intelligence (AI)artificial intelligence (AI) in RADAR remote sensing, with applications such as enhanced target detectiontarget detection and noise reduction. Various advanced techniques in the field of AI, including convolutional neural networks (CNNsCNNs) and recurrent neural networks, have improved the efficiency of RADAR systemsRADAR systems. Autoencoders and generative adversarial networks can be used to enhance the quality of images. These techniques also accelerate real-time datareal-time data processing, improving RADAR system responsiveness in disaster monitoringdisaster monitoring and autonomous navigation. This chapter addresses these challenges, explores methods to enhance AI models for RADAR applications, and outlines future research directions. Integrating AI into RADAR systems enhances their intelligence, autonomy, and ability to address climate monitoring, defense, and smart city application challenges.
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
