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020 _a9789819989973
_9978-981-99-8997-3
024 7 _a10.1007/978-981-99-8997-3
_2doi
050 4 _aG70.212-.217
072 7 _aRGW
_2bicssc
072 7 _aSCI030000
_2bisacsh
072 7 _aRGW
_2thema
100 1 _aChen, Weitao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_969179
245 1 0 _aRemote Sensing Intelligent Interpretation for Geology
_h[electronic resource] :
_bFrom Perspective of Geological Exploration /
_cby Weitao Chen, Xianju Li, Xuwen Qin, Lizhe Wang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXI, 235 p. 98 illus., 76 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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 _aThis 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.
650 0 _aGeographic information systems.
_961967
650 0 _aGeology.
_965838
650 0 _aMachine learning.
_955059
650 0 _aMineralogy.
_966552
650 1 4 _aGeographical Information System.
_966422
650 2 4 _aGeology.
_965838
650 2 4 _aMachine Learning.
_955059
650 2 4 _aMineralogy.
_966552
700 1 _aLi, Xianju.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_969180
700 1 _aQin, Xuwen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_969181
700 1 _aWang, Lizhe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_969182
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819989966
776 0 8 _iPrinted edition:
_z9789819989980
776 0 8 _iPrinted edition:
_z9789819989997
856 4 0 _uhttps://doi.org/10.1007/978-981-99-8997-3
912 _aZDB-2-EES
912 _aZDB-2-SXEE
999 _c57077
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