04305nam a22005775i 4500 978-981-99-8997-3 DE-He213 20251030110913.0 cr nn 008mamaa 240103s2024 si | s |||| 0|eng d 9789819989973 978-981-99-8997-3 10.1007/978-981-99-8997-3 doi G70.212-.217 RGW bicssc SCI030000 bisacsh RGW thema Chen, Weitao. author. aut http://id.loc.gov/vocabulary/relators/aut 69179 Remote Sensing Intelligent Interpretation for Geology [electronic resource] : From Perspective of Geological Exploration / by Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XI, 235 p. 98 illus., 76 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Geographic information systems. 61967 Geology. 65838 Machine learning. 55059 Mineralogy. 66552 Geographical Information System. 66422 Geology. 65838 Machine Learning. 55059 Mineralogy. 66552 Li, Xianju. author. aut http://id.loc.gov/vocabulary/relators/aut 69180 Qin, Xuwen. author. aut http://id.loc.gov/vocabulary/relators/aut 69181 Wang, Lizhe. author. aut http://id.loc.gov/vocabulary/relators/aut 69182 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819989966 Printed edition: 9789819989980 Printed edition: 9789819989997 https://doi.org/10.1007/978-981-99-8997-3 ZDB-2-EES ZDB-2-SXEE 57077 57077