Remote Sensing Intelligent Interpretation for Mine Geological Environment

Geben Sie eine Produktbewertung ab.

From Land Use and Land Cover Perspective

Autor: Weitao Chen

CHF 206.00

ISBN: 978-981-1937-41-5

Einband: Kartonierter Einband (Kt)

Verfügbarkeit: Folgt in ca. 5 Arbeitstagen

Menge:

+ -

This book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of ¿target detection¿scene classification¿semantic segmentation."

Taking Chinäs Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation.

The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research.

Autor	Chen, Weitao / Wang, Lizhe / Li, Xianju
Verlag	Springer Nature Singapore
Einband	Kartonierter Einband (Kt)
Erscheinungsjahr	2023
Seitenangabe	260 S.
Lieferstatus	Folgt in ca. 5 Arbeitstagen
Ausgabekennzeichen	Englisch
Abbildungen	Paperback
Masse	H23.5 cm x B15.5 cm x D1.4 cm 447 g
Auflage	23001 A. 1st ed. 2022

Über den Autor Weitao Chen

Dr. Weitao Chen (Member, IEEE) is a full professor at the School of Computer Science, China Univ. of Geosciences (CUG). He received M.E. in 2006 and Doctor from China Univ. of Geosciences in 2012. He has published more than 50 peer-reviewed technical papers in international journals. His main research interests include machine learning and remote sensing of geo-environment. Prof. Chen is a member of IEEE and served as editor on board and reviewer of several international journals. He was awarded the Land and Resources Science and Technology Progress Award (second prize in 2019) and the Science and Technology Award (second prize) of China command and control society (second prize in 2020). He was awarded "cradle plan" talent project of the China University of Geosciences and the "Chenguang plan" talent project of Youth Science and Technology in Wuhan, Hubei Province.Dr. Xianju Li is an associate professor at the School of Computer Science, the China University of Geosciences (CUG). He received B.E. in 2009, M.E. in 2012, and Ph.D. in 2016 from the China University of Geosciences, Wuhan. He has more than ten years of experience in geological remote sensing with machine learning and deep learning techniques. He has acquired two 2nd Prizes at the provincial level and published more than 30 peer-reviewed papers.Dr. Xuwen Qin is a researcher in the China Geological Survey who has been awarded as "Li Siguang Scholar." He has been researching on the theory, technical equipment, and applications of remote sensing and physical detection in geological survey and has made breakthroughs in many key technologies. He also acquired many high-level achievements in the realm of terrestrial and deep-ocean geological hazards. Related works were collected in the China Top 10 Science and Technology Development, selected by the members of the Chinese Academy of Sciences and the Chinese Academy of Engineering. In addition, he has acquired four 1st Prizes and one 2nd Prize at the provincial level, published 16 monographs and 43 SCI academic papers, and has been awarded 12 international/national invention patents.Dr. Lizhe Wang is a full professor at the School of Computer Science, China Univ. of Geosciences (CUG). He received the B.E. and M.E. degrees from Tsinghua University, Beijing, China, and the Doctor of Eng. degree from University Karlsruhe (Magna Cum Laude), Germany. He is a ChuTian chair professor at the School of Computer Science, China University of Geosciences, Wuhan, China. His research interests include HPC, e-Science, and remote sensing image processing. Prof. Wang is a fellow of IET, IEEE, SPIE, and British Computer Society. He was awarded the Land and Resources Science and Technology Progress Award (second prize in 2019), and the Science and Technology Award (second prize) of China command and control society (second prize in 2020). He was also awarded National Science Fund for Distinguished Young Scholars in 2019 and European Academy of Sciences in 2022.