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Deep learning for multi-sensor Earth observation /

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 v...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Saha, Sudipan (Editor)
Format: eBook
Language:English
Published: Amsterdam, Netherlands ; Cambridge, MA : Elsevier, [2025]
Series:Earth observation series.
Subjects:
Earth sciences > Remote sensing.
Deep learning (Machine learning)
Apprentissage profond.
Sciences de la terre > Télédétection.
Electronic books.
Online Access:Connect to the full text of this electronic book
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
  • 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
  • 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
  • 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
  • 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