Fusion of scatterometer and optical remote sensing: enhanced classification and change detection
-
Rajinder Kaur
,
Sartajvir Singh
and
Ganesh Kumar Sethi
Abstract
Earth observation via high or enhanced resolution remote sensing satellite imagery is necessary to observe the Earth’s resources accurately. Nevertheless, a single satellite sensor’s remote sensing images do not offer significant information. Pan-sharpening techniques have become more common in producing multispectral images with great spatial resolution. The optical and microwave datasets are fused using pan-sharpeningpan-sharpening techniques to provide high-resolution products with substantial spectral and spatial information. These datasets were combined using the Brovey transform to create high-resolution pictures. A support vector machine classifierSVM classifier was used first to classify the photos, and a change detectionchange detection model was then used to classify images to create change maps. Finally, using MODISMODIS data as the validation reference, an accuracy study was conducted to gauge the outcomes. This research aims to enhance natural hazard monitoring and forecasting in mountainous regions.
Abstract
Earth observation via high or enhanced resolution remote sensing satellite imagery is necessary to observe the Earth’s resources accurately. Nevertheless, a single satellite sensor’s remote sensing images do not offer significant information. Pan-sharpening techniques have become more common in producing multispectral images with great spatial resolution. The optical and microwave datasets are fused using pan-sharpeningpan-sharpening techniques to provide high-resolution products with substantial spectral and spatial information. These datasets were combined using the Brovey transform to create high-resolution pictures. A support vector machine classifierSVM classifier was used first to classify the photos, and a change detectionchange detection model was then used to classify images to create change maps. Finally, using MODISMODIS data as the validation reference, an accuracy study was conducted to gauge the outcomes. This research aims to enhance natural hazard monitoring and forecasting in mountainous regions.
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
