000			02838 a2200301 4500
001			11513
003			IN-BhIIT
005			20260804123521.0
008			260804b |||||||| |||| 00| 0 eng d
020			_a9780443264849 (hardback)
040			_aIN-BhIIT
041			_aeng
082			_a621.3678 _bSAH/D
245			_aDeep learning for multi-sensor Earth observation / _cedited by Sudipan Saha.
260			_aAmsterdam : _bElsevier, _c2025.
300			_axv, 436 pages : _billustrations ; _c24 cm.
490			_aEarth observation series.
500			_aIncludes contributions by international experts in remote sensing and artificial intelligence.
504			_aIncludes bibliographical references and index.
505			_aPart 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.
520			_aThe 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.
650			_aRemote sensing.
650			_aEarth sciences _xRemote sensing. _928221
650			_aDeep learning (Machine learning) _921147
650			_aArtificial intelligence. _9739
700			_aSaha, Sudipan, _eeditor. _928222
942			_cTRB
999			_c15573 _d15573