Machine learning for planetary science /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Helbert, Joern (Editor ), D'Amore, Mario (Editor ), Aye, Michael (Editor ), Kerner, Hannah (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier, 2022.
Temas:
Planetary science > Data processing.
Machine learning.
Plan�etologie > Informatique.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Machine Learning for Planetary Science
  • Copyright
  • Contents
  • Contributors
  • Foreword
  • References
  • 1 Introduction to machine learning
  • 1.1 Overview of machine learning methods
  • 1.2 Supervised learning
  • 1.2.1 Classification
  • 1.2.2 Regression
  • 1.3 Unsupervised learning
  • 1.3.1 Clustering
  • 1.3.2 Dimensionality reduction
  • 1.4 Semisupervised learning
  • 1.4.1 Self-training
  • 1.4.2 Self-training with Expectation Maximization
  • 1.4.3 Cotraining
  • 1.5 Active learning
  • 1.5.1 Uncertainty sampling
  • 1.5.2 Query-by-committee
  • 1.6 Popular machine learning methods
  • 1.6.1 Principal component analysis
  • 1.6.2 K-means clustering
  • 1.6.3 Support vector machines
  • 1.6.4 Decision trees and random forests
  • 1.6.5 Neural networks
  • 1.7 Data set preparation
  • References
  • 2 The new and unique challenges of planetary missions
  • 2.1 Introduction
  • 2.1.1 50 years of Mercury exploration
  • 2.1.2 Challenges of large and complex data return
  • 2.1.3 Facing the unknown
  • 2.1.4 Machine learning for planetary science
  • References
  • 3 Finding and reading planetary data
  • 3.1 Data acquisition in planetary science
  • 3.1.1 Introduction
  • 3.1.2 Data processing levels
  • 3.1.3 PDS
  • 3.1.3.1 Organizational structure within a node
  • Releases and volumes
  • EDR and RDR
  • PDS4 collections and bundles
  • 3.1.4 ESA's Planetary Science Archive
  • 3.1.5 Reading data with Python
  • 3.1.5.1 Example reading of PDS3 data
  • 3.1.5.2 Troubleshooting data reading
  • 3.1.6 Spaces to watch
  • 3.1.6.1 PDR
  • 3.1.6.2 PlanetaryPy
  • 3.1.6.3 OpenPlanetary
  • 4 Introduction to the Python Hyperspectral Analysis Tool (PyHAT)
  • 4.1 Introduction
  • 4.2 PyHAT library architecture
  • 4.3 PyHAT orbital
  • 4.3.1 Compact Reconnaissance Imaging Spectrometer for Mars (CRISM)
  • 4.3.2 Moon Mineralogy Mapper (M3)
  • 4.3.3 Kaguya Spectral Profiler
  • 4.4 PyHAT in-situ
  • 4.4.1 Baseline removal example
  • 4.4.2 Regression analysis example
  • 4.4.3 Data exploration example
  • 4.4.4 Calibration transfer
  • 4.5 Conclusion
  • Acronyms
  • Acknowledgments
  • References
  • 5 Tutorial: how to access, process, and label PDS image data for machine learning
  • 5.1 Introduction
  • 5.2 Access to PDS data products
  • 5.2.1 PDS Image Atlas
  • 5.2.2 PDS Imaging Node Data Portal
  • 5.3 Preprocessing PDS data products into standard image formats
  • 5.3.1 PDS image data products
  • 5.3.2 PDS browse images
  • 5.3.3 Converting PDS image data products
  • 5.4 Labeling image data
  • 5.4.1 Publicly available labeled image data sets
  • 5.4.2 Tools for labeling image data
  • 5.5 Example PDS image classifier results
  • 5.5.1 Train, validation, and test sets
  • 5.5.2 Model fine-tuning
  • 5.5.3 Model calibration and performance
  • 5.5.4 Access to HiRISENet classification results
  • 5.6 Summary
  • Acknowledgments
  • References

Ejemplares similares