02838 a2200301 4500 11513 IN-BhIIT 20260804123521.0 260804b |||||||| |||| 00| 0 eng d 9780443264849 (hardback) IN-BhIIT eng 621.3678 SAH/D 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. Remote sensing. Earth sciences Remote sensing. 28221 Deep learning (Machine learning) 21147 Artificial intelligence. 739 Saha, Sudipan, editor. 28222 TRB 15573 15573 0 0 ddc 0 0 SECS CLIITBBS CLIITBBS TRB 2026-07-28 21 10482.81 0 621.3678 SAH/D 11513 2026-07-28 00:00:00 14360.02 2026-07-28 TRB