Machine learning for planetary science

שמור ב:

מידע ביבליוגרפי
מחבר ראשי:	Helbert, Joern.
מחברים אחרים:	D'Amore, Mario., Aye, Michael).
פורמט:	E-Book
שפה:	Anglais
יצא לאור:	Amsterdam : Elsevier, 2022.
נושאים:	Apprentissage automatique Planétologie > Informatique Machine learning Planetary science > Data processing
Autres localisations:	Voir dans le Sudoc
סיכום:	Machine learning for planetary science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation.
גישה מקוונת:	Accès à l'E-book
פריטים קשורים:	Autre support: Machine learning for planetary science

תוכן הענינים:

1. Introduction to machine learning
2. The new and unique challenges of planetary missions
3. Finding and reading planetary data
4. Introduction to the Python Hyperspectral Analysis Tool (PyHAT )
5. Tutorial : how to access, process, and label PDS image data for machine learning
6. Planetary image inpainting by learning mode-specific regression models
7. Automated surface mapping via unsupervised learning and classification of Mercury Visible-Near-Infrared reflectance spectra
8. Mapping storms on Saturn
9. Machine learning for planetary rovers
10. Combining machine-learned regression models with Bayesian inference to interpret remote sensing data

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