Integrating Sentinel-1 satellite data with machine learning for land use classification
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Narayan Vyas
Abstract
The availability of radio detection and ranging (RADAR) remote sensing data has changed the Earth observationEarth observation by allowing information to be obtained independently of the weather and environmental conditions. RADAR-based satellites, such as Sentinel-1Sentinel-1 and SCATSATSCATSAT, are beneficial because they can penetrate clouds and work regardless of whether it is night or day, making them indispensable surface mapping sensors. The European Space Agency’s (ESA) Sentinel-1 satellite is a RADAR-based Earth observation satellite whose dual-polarization (vertical-vertical and vertical-horizontalVV and VH) synthetic aperture radar (SAR) provides high temporal resolution with adequate spatial resolution that makes it suitable for monitoring land use and land coverland cover (LULC) changes. The study area in this research is Kota district, situated in the southeastern part of Rajasthan, India, which has a semiarid climate and a mixed area of agriculture, natural vegetation, and waterbodies. Google Earth Engine was used to process and classify Sentinel-1 data collected from February 1, 2024, to February 27, 2024, into several LULC categories, including water, built-up, and land. This study uses random forest, an ensemble machine learningmachine learning (ML) model, for LULC classification. When used to create a land use classification map, it exhibited an overall accuracyoverall accuracy (OA) of 90.57% and a kappa coefficient of 85.81%, demonstrating near-perfect agreement between the model and actual data.
Abstract
The availability of radio detection and ranging (RADAR) remote sensing data has changed the Earth observationEarth observation by allowing information to be obtained independently of the weather and environmental conditions. RADAR-based satellites, such as Sentinel-1Sentinel-1 and SCATSATSCATSAT, are beneficial because they can penetrate clouds and work regardless of whether it is night or day, making them indispensable surface mapping sensors. The European Space Agency’s (ESA) Sentinel-1 satellite is a RADAR-based Earth observation satellite whose dual-polarization (vertical-vertical and vertical-horizontalVV and VH) synthetic aperture radar (SAR) provides high temporal resolution with adequate spatial resolution that makes it suitable for monitoring land use and land coverland cover (LULC) changes. The study area in this research is Kota district, situated in the southeastern part of Rajasthan, India, which has a semiarid climate and a mixed area of agriculture, natural vegetation, and waterbodies. Google Earth Engine was used to process and classify Sentinel-1 data collected from February 1, 2024, to February 27, 2024, into several LULC categories, including water, built-up, and land. This study uses random forest, an ensemble machine learningmachine learning (ML) model, for LULC classification. When used to create a land use classification map, it exhibited an overall accuracyoverall accuracy (OA) of 90.57% and a kappa coefficient of 85.81%, demonstrating near-perfect agreement between the model and actual data.
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
