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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.
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.
2. Dealing with small labeled datasets (semi-supervised learning, active learning)
3. Selecting a methodology and evaluation metrics
4. Interpreting and explaining model behavior
5. Hyperparameter optimization and training neural networks Part II: Methods of machine learning6. The new and unique challenges of planetary missions
7. Data acquisition (PDS nodes, etc.) and Data types, projections, processing, units, etc. Part III: Useful tools for machine learning projects in planetary science8. The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface
9. Getting data from the PDS, pre-processing, and labeling it Part IV: Case studies10. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning and/or Data Restoration
11. Surface mapping via unsupervised learning and clustering of Mercury's Visible-Near-Infrared reflectance spectra
12. Mapping Saturn using deep learning
13. Artificial Intelligence for Planetary Data Analytics - Computer Vision to Boost Detection and Analysis of Jupiter's White Ovals in Images Acquired by the Jiram Spectrometer
| Erscheinungsjahr: | 2022 |
|---|---|
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9780128187210 |
| ISBN-10: | 0128187212 |
| Sprache: | Englisch |
| Herstellernummer: | C2018-0-04220-6 |
| Redaktion: |
Helbert, Joern
D'Amore, Mario Aye, Michael Kerner, Hannah |
| Hersteller: |
Elsevier
Elsevier Science & Technology |
| Verantwortliche Person für die EU: | Zeitfracht Medien GmbH, Ferdinand-Jühlke-Str. 7, D-99095 Erfurt, produktsicherheit@zeitfracht.de |
| Abbildungen: | Approx. 110 illustrations |
| Maße: | 11 x 152 x 229 mm |
| Von/Mit: | Joern Helbert (u. a.) |
| Erscheinungsdatum: | 25.03.2022 |
| Gewicht: | 0,39 kg |
2. Dealing with small labeled datasets (semi-supervised learning, active learning)
3. Selecting a methodology and evaluation metrics
4. Interpreting and explaining model behavior
5. Hyperparameter optimization and training neural networks Part II: Methods of machine learning6. The new and unique challenges of planetary missions
7. Data acquisition (PDS nodes, etc.) and Data types, projections, processing, units, etc. Part III: Useful tools for machine learning projects in planetary science8. The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface
9. Getting data from the PDS, pre-processing, and labeling it Part IV: Case studies10. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning and/or Data Restoration
11. Surface mapping via unsupervised learning and clustering of Mercury's Visible-Near-Infrared reflectance spectra
12. Mapping Saturn using deep learning
13. Artificial Intelligence for Planetary Data Analytics - Computer Vision to Boost Detection and Analysis of Jupiter's White Ovals in Images Acquired by the Jiram Spectrometer
| Erscheinungsjahr: | 2022 |
|---|---|
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9780128187210 |
| ISBN-10: | 0128187212 |
| Sprache: | Englisch |
| Herstellernummer: | C2018-0-04220-6 |
| Redaktion: |
Helbert, Joern
D'Amore, Mario Aye, Michael Kerner, Hannah |
| Hersteller: |
Elsevier
Elsevier Science & Technology |
| Verantwortliche Person für die EU: | Zeitfracht Medien GmbH, Ferdinand-Jühlke-Str. 7, D-99095 Erfurt, produktsicherheit@zeitfracht.de |
| Abbildungen: | Approx. 110 illustrations |
| Maße: | 11 x 152 x 229 mm |
| Von/Mit: | Joern Helbert (u. a.) |
| Erscheinungsdatum: | 25.03.2022 |
| Gewicht: | 0,39 kg |
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