Deep learning for multi-sensor Earth observation / (Record no. 15573)

MARC details
000 -LEADER
fixed length control field 02838 a2200301 4500
001 - CONTROL NUMBER
control field 11513
003 - CONTROL NUMBER IDENTIFIER
control field IN-BhIIT
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260804123521.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260804b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780443264849 (hardback)
040 ## - CATALOGING SOURCE
Original cataloging agency IN-BhIIT
041 ## - LANGUAGE CODE
Language code of text eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.3678
Book number SAH/D
245 ## - TITLE STATEMENT
Title Deep learning for multi-sensor Earth observation /
Statement of responsibility, etc edited by Sudipan Saha.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Amsterdam :
Name of publisher Elsevier,
Year of publication 2025.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xv, 436 pages :
Other physical details(ill.) illustrations ;
Dimensions(size) 24 cm.
490 ## - SERIES STATEMENT
Series statement Earth observation series.
500 ## - GENERAL NOTE
General note Includes contributions by international experts in remote sensing and artificial intelligence.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Remote sensing.
Topical Term Earth sciences
General subdivision Remote sensing.
Topical Term Deep learning (Machine learning)
Topical Term Artificial intelligence.
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Saha, Sudipan,
Relator term editor.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Technical Reference Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Cost, replacement price Price effective from Koha item type
Not withdrawn Not Lost Dewey Decimal Classification not damaged   SECS Central Library, IIT Bhubaneswar Central Library, IIT Bhubaneswar Technical Reference Book Section 28/07/2026 21 10482.81   621.3678 SAH/D 11513 28/07/2026 14360.02 28/07/2026 Technical Reference Book
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