rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep learning for multi-sensor Earth observation
  name:
    name part: Saha, Sudipan
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2025
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xv, 436 pages) : illustrations (some color), maps (some color).
  abstract: Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development. Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.
  table of contents: Front Cover -- Deep Learning for Multi-Sensor Earth Observation -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- I Introduction to multi-sensor data and artificial intelligence -- 1 Deep learning for multi-sensor Earth observation: introductory notes -- 1.1 Introduction -- 1.2 Different sensors and modalities -- 1.3 Benefits of fusion -- 1.4 Traditional multi-sensor fusion -- 1.5 Emergence of deep learning -- 1.6 Motivation and contents of the book -- References -- 2 A basic introduction to deep learning -- 2.1 Introduction -- 2.2 Different learning paradigms
  table of contents: 2.3 CNN -- 2.4 Image classification -- 2.4.1 LeNet-5 -- 2.4.2 AlexNet -- 2.4.3 VGGNet -- 2.4.4 InceptionNet -- 2.4.5 ResNet -- 2.4.6 DenseNet -- 2.4.7 MobileNet -- 2.5 Semantic segmentation -- 2.6 Target detection -- 2.7 From attention to transformers -- 2.8 Autoencoder and generative models -- 2.9 Practices and tricks -- 2.9.1 Appropriate learning task -- 2.9.2 Data quality -- 2.9.3 Choice of appropriate model -- 2.9.4 Hyperparameter tuning -- 2.9.5 Regularization -- 2.9.6 Loss functions -- 2.10 Conclusion -- References -- II Artificial intelligence for sensor-specific data analysis and fusion
  table of contents: 3 Deep learning processing of remotely sensed multi-spectral images -- 3.1 Introduction -- 3.1.1 Remotely sensed multi-spectral images -- 3.1.2 Deep learning methods taxonomy -- 3.1.3 Chapter overview -- 3.2 Image preprocessing techniques -- 3.2.1 Super-resolution -- 3.2.2 Image fusion -- 3.2.2.1 Homogeneous fusion -- 3.2.2.2 Spatiotemporal fusion -- 3.2.2.3 Heterogeneous fusion -- 3.2.3 Segmentation -- 3.2.4 Image registration and enhancement -- 3.2.4.1 Registration -- 3.2.4.2 Denoising -- 3.3 Image analysis -- 3.3.1 Change detection -- 3.3.2 Object detection and recognition
  table of contents: 3.3.3 Scene classification -- 3.4 Conclusions and perspectives -- Acknowledgments -- References -- 4 Deep learning and hyperspectral images -- 4.1 Introduction -- 4.2 Image classification -- 4.2.1 Limited label learning for HSI classification -- 4.2.1.1 Self-supervised learning for HSI classification -- 4.2.1.2 Semi-supervised learning for HSI classification -- 4.2.1.3 Meta learning for HSI classification -- 4.3 Dimensionality reduction -- 4.3.1 Band selection -- 4.3.2 Feature extraction -- 4.4 Unmixing -- 4.5 Image enhancement -- 4.5.1 Denoising -- 4.5.2 Super-resolution -- 4.5.3 Inpainting
  table of contents: 4.6 Change detection -- 4.7 Future direction -- 4.8 Summary -- References -- 5 Synthetic aperture radar image analysis in era of deep learning -- 5.1 Introduction -- 5.2 SAR despeckling -- 5.3 SAR image classification -- 5.4 SAR semantic segmentation -- 5.5 SAR target detection -- 5.6 SAR domain adaptation -- 5.7 SAR-optical transcoding -- 5.8 PolSAR image analysis -- 5.9 InSAR data analysis -- 5.10 SAR analysis benefits from other sensors -- 5.11 Conclusion -- References -- 6 Deep learning with lidar for Earth observation -- 6.1 Introduction
  note: edited by Sudipan Saha.
  note: Includes bibliographical references and index.
  subject:
    topic: Earth sciences
    topic: Remote sensing
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Apprentissage profond
  subject:
    topic: Sciences de la terre
    topic: Télédétection
  classification: QE33.2.R4 D44 2025
  classification: Q325.73 .D44 2025
  classification: 550.28563/1
  classification: 006.3/1
  related item:
    identifier: (OCoLC)1453719304
  related item:
    title info:
      title: Earth observation series
  identifier: 0443264856
  identifier: 9780443264856
  identifier: 9780443264849
  identifier: 0443264848
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 250217
    record change date: 20260327181120.3
    record identifier: in00005774327
    language of cataloging:
      language term: eng