Deep learning for multi-sensor Earth observation /
Deep learning for multi-sensor Earth observation /
edited by Sudipan Saha.
- Amsterdam : Elsevier, 2025.
- xv, 436 pages : illustrations ; 24 cm.
- Earth observation series. .
Includes contributions by international experts in remote sensing and artificial intelligence.
Includes bibliographical references and index.
Part I. Introduction to multi-sensor data and artificial intelligence -- Deep learning for multi-sensor Earth observation: introductory notes -- A basic introduction to deep learning -- Part II. Artificial intelligence for sensor-specific data analysis and fusion -- Deep learning processing of remotely sensed multispectral images -- Deep learning and hyperspectral images -- Synthetic aperture radar image analysis in era of deep learning -- Deep learning with LiDAR for Earth observation -- Several sensors and modalities -- Part III. Advanced concepts and architectures -- Self-supervised learning for multimodal Earth observation data -- Vision transformers and multi-sensor Earth observation -- Graph neural networks for multi-sensor Earth observation -- Uncertainty quantification in deep neural networks for multi-sensor Earth observation -- Part IV. Multi-sensor deep learning applications -- Multi-sensor deep learning for change detection -- Multi-sensor deep learning for glacier mapping -- Deep learning in multi-sensor agriculture and crop management -- Miscellaneous applications of deep learning-based multi-sensor Earth observation -- Multi-sensor Earth observation: outlook.
The book Deep Learning for Multi-Sensor Earth Observation presents state-of-the-art methods for integrating deep learning with multisensor remote sensing data. Covering multispectral, hyperspectral, SAR, and LiDAR imagery, it explains modern AI techniques—including self-supervised learning, vision transformers, graph neural networks, and uncertainty quantification—and demonstrates their application in change detection, glacier mapping, agriculture, and environmental monitoring. The book serves as a comprehensive reference for researchers, graduate students, and professionals in Earth observation and geospatial artificial intelligence.
9780443264849 (hardback)
Remote sensing.
Earth sciences--Remote sensing.
Deep learning (Machine learning)
Artificial intelligence.
621.3678 / SAH/D
Includes contributions by international experts in remote sensing and artificial intelligence.
Includes bibliographical references and index.
Part I. Introduction to multi-sensor data and artificial intelligence -- Deep learning for multi-sensor Earth observation: introductory notes -- A basic introduction to deep learning -- Part II. Artificial intelligence for sensor-specific data analysis and fusion -- Deep learning processing of remotely sensed multispectral images -- Deep learning and hyperspectral images -- Synthetic aperture radar image analysis in era of deep learning -- Deep learning with LiDAR for Earth observation -- Several sensors and modalities -- Part III. Advanced concepts and architectures -- Self-supervised learning for multimodal Earth observation data -- Vision transformers and multi-sensor Earth observation -- Graph neural networks for multi-sensor Earth observation -- Uncertainty quantification in deep neural networks for multi-sensor Earth observation -- Part IV. Multi-sensor deep learning applications -- Multi-sensor deep learning for change detection -- Multi-sensor deep learning for glacier mapping -- Deep learning in multi-sensor agriculture and crop management -- Miscellaneous applications of deep learning-based multi-sensor Earth observation -- Multi-sensor Earth observation: outlook.
The book Deep Learning for Multi-Sensor Earth Observation presents state-of-the-art methods for integrating deep learning with multisensor remote sensing data. Covering multispectral, hyperspectral, SAR, and LiDAR imagery, it explains modern AI techniques—including self-supervised learning, vision transformers, graph neural networks, and uncertainty quantification—and demonstrates their application in change detection, glacier mapping, agriculture, and environmental monitoring. The book serves as a comprehensive reference for researchers, graduate students, and professionals in Earth observation and geospatial artificial intelligence.
9780443264849 (hardback)
Remote sensing.
Earth sciences--Remote sensing.
Deep learning (Machine learning)
Artificial intelligence.
621.3678 / SAH/D