Remote Sensing Intelligent Interpretation for Geology From Perspective of Geological Exploration /
This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remo...
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| Auteurs principaux: | , , , |
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| Format: | Électronique eBook |
| Langue: | anglais |
| Publié: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Édition: | 1st ed. 2024. |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-981-99-8997-3 |
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| 100 | 1 | |a Chen, Weitao. |e author. |0 (orcid)0000-0002-6272-1618 |1 https://orcid.org/0000-0002-6272-1618 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Remote Sensing Intelligent Interpretation for Geology |h [electronic resource] : |b From Perspective of Geological Exploration / |c by Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XI, 235 p. 98 illus.). |b 76 illus. in color. | ||
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| 341 | 0 | |b Single logical reading order |2 onix | |
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| 505 | 0 | |a Chapter 1. Geological remote sensing: An overview -- Chapter 2. Remote sensing interpretation signs of geology -- Chapter 3. Geological remote sensing dataset construction for multi-level tasks -- Chapter 4. Lithology pixel classification using deep convolutional network and remote sensing -- Chapter 5 Lithology scene classification using deep learning and remote sensing -- Chapter 6. Lithology semantic segmentation methods using deep learning and remote sensing -- Chapter 7. Lithology intelligent classification using prior knowledge-based and remote sensing -- Chapter 8. Lithology intelligent classification using transfer learning and remote sensing -- Chapter 9. Remote sensing intelligent identification of fault tectonics -- Chapter 10. Mineral abundance inversion based on sparse unmixing theory and hyperspectral remote sensing -- Chapter 11. Concluding remarks. | |
| 520 | |a This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing. The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Geographic information systems. | |
| 650 | 0 | |a Geology. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Mineralogy. | |
| 650 | 1 | 4 | |a Geographical Information System. |
| 650 | 2 | 4 | |a Geology. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Mineralogy. |
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| 700 | 1 | |a Qin, Xuwen. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Wang, Lizhe. |e author. |0 (orcid)0000-0003-2766-0845 |1 https://orcid.org/0000-0003-2766-0845 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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