東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Remote sensing intelligent interpretation for geology = from perspective of geological exploration /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote sensing intelligent interpretation for geology/ by Weitao Chen ... [et al.].
Reminder of title:	from perspective of geological exploration /
other author:	Chen, Weitao.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xi, 235 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Geology - Remote sensing. -
Online resource:	https://doi.org/10.1007/978-981-99-8997-3
ISBN:	9789819989973

Remote sensing intelligent interpretation for geology = from perspective of geological exploration /
Remote sensing intelligent interpretation for geologyfrom perspective of geological exploration /[electronic resource] :by Weitao Chen ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xi, 235 p. :ill., digital ;24 cm.

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.

ISBN: 9789819989973

Standard No.: 10.1007/978-981-99-8997-3doiSubjects--Topical Terms:

561019
Geology
--Remote sensing.

LC Class. No.: QE33.2.R4

Dewey Class. No.: 551.0285

Remote sensing intelligent interpretation for geology = from perspective of geological exploration /
LDR:03076nmm a2200325 a 4500 001 2367713
003 DE-He213
005 20240103223439.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819989973 $q (electronic bk.)
020 $a 9789819989966 $q (paper)
024 7 $a 10.1007/978-981-99-8997-3 $2 doi
035 $a 978-981-99-8997-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QE33.2.R4
072 7 $a RGW $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a RGW $2 thema
082 0 4 $a 551.0285 $2 23
090 $a QE33.2.R4 $b R389 2024
245 0 0 $a Remote sensing intelligent interpretation for geology $h [electronic resource] : $b from perspective of geological exploration / $c by Weitao Chen ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xi, 235 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Geology $x Remote sensing. $3 561019
650 1 4 $a Geographical Information System. $3 3538564
650 2 4 $a Geology. $3 516570
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Mineralogy. $3 516743
700 1 $a Chen, Weitao. $3 3604144
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-8997-3
950 $a Earth and Environmental Science (SpringerNature-11646)