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  <titleInfo>
    <title>Deep learning for multi-sensor Earth observation</title>
  </titleInfo>
  <name type="personal">
    <namePart>Saha, Sudipan</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
    </role>
  </name>
  <typeOfResource/>
  <originInfo>
    <place>
      <placeTerm type="text">Amsterdam</placeTerm>
    </place>
    <publisher>Elsevier</publisher>
    <dateIssued>2025</dateIssued>
    <issuance/>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>xv, 436 pages :  illustrations ;  24 cm.</extent>
  </physicalDescription>
  <abstract>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.</abstract>
  <tableOfContents>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.</tableOfContents>
  <note type="statement of responsibility">edited by Sudipan Saha.</note>
  <note>Includes contributions by international experts in remote sensing and artificial intelligence.</note>
  <note>Includes bibliographical references and index.</note>
  <subject>
    <topic>Remote sensing</topic>
  </subject>
  <subject>
    <topic>Earth sciences</topic>
    <topic>Remote sensing</topic>
  </subject>
  <subject>
    <topic>Deep learning (Machine learning)</topic>
  </subject>
  <subject>
    <topic>Artificial intelligence</topic>
  </subject>
  <classification authority="ddc">621.3678 SAH/D</classification>
  <identifier type="isbn">9780443264849 (hardback)</identifier>
  <recordInfo>
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    <recordCreationDate encoding="marc">260804</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260804123521.0</recordChangeDate>
    <recordIdentifier source="IN-BhIIT">11513</recordIdentifier>
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