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...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer
[2024]
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xi, 235 Seiten Illustrationen |
| ISBN: | 9789819989966 9789819989997 |
Internformat
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| 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 | ||
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Datensatz im Suchindex
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| id | DE-604.BV049530813 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:26:56Z |
| indexdate | 2024-07-10T10:09:54Z |
| institution | BVB |
| isbn | 9789819989966 9789819989997 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034876451 |
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| owner_facet | DE-29 |
| physical | xi, 235 Seiten Illustrationen |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| record_format | marc |
| spelling | Chen, Weitao Verfasser aut Remote sensing intelligent interpretation for geology from perspective of geological exploration Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang Singapore Springer [2024] xi, 235 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier 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 Geology Machine learning Mineralogy Geographic information systems Fernerkundung (DE-588)4016796-3 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Geologie (DE-588)4020227-6 gnd rswk-swf Geologie (DE-588)4020227-6 s Fernerkundung (DE-588)4016796-3 s Datenverarbeitung (DE-588)4011152-0 s DE-604 Li, Xianju Verfasser aut Qin, Xuwen Verfasser aut Wang, Lizhe 1974- Verfasser (DE-588)1194415105 aut Erscheint auch als Online-Ausgabe 978-981-99-8997-3 |
| spellingShingle | Chen, Weitao Li, Xianju Qin, Xuwen Wang, Lizhe 1974- Remote sensing intelligent interpretation for geology from perspective of geological exploration Geology Machine learning Mineralogy Geographic information systems Fernerkundung (DE-588)4016796-3 gnd Datenverarbeitung (DE-588)4011152-0 gnd Geologie (DE-588)4020227-6 gnd |
| subject_GND | (DE-588)4016796-3 (DE-588)4011152-0 (DE-588)4020227-6 |
| title | Remote sensing intelligent interpretation for geology from perspective of geological exploration |
| title_auth | Remote sensing intelligent interpretation for geology from perspective of geological exploration |
| title_exact_search | Remote sensing intelligent interpretation for geology from perspective of geological exploration |
| title_exact_search_txtP | Remote sensing intelligent interpretation for geology from perspective of geological exploration |
| title_full | Remote sensing intelligent interpretation for geology from perspective of geological exploration Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang |
| title_fullStr | Remote sensing intelligent interpretation for geology from perspective of geological exploration Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang |
| title_full_unstemmed | Remote sensing intelligent interpretation for geology from perspective of geological exploration Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang |
| title_short | Remote sensing intelligent interpretation for geology |
| title_sort | remote sensing intelligent interpretation for geology from perspective of geological exploration |
| title_sub | from perspective of geological exploration |
| topic | Geology Machine learning Mineralogy Geographic information systems Fernerkundung (DE-588)4016796-3 gnd Datenverarbeitung (DE-588)4011152-0 gnd Geologie (DE-588)4020227-6 gnd |
| topic_facet | Geology Machine learning Mineralogy Geographic information systems Fernerkundung Datenverarbeitung Geologie |
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