Remote Sensing Intelligent Interpretation for Geology: From Perspective of Geological Exploration (地質遙感智能解讀：從地質勘探的角度)
Chen, Weitao, Li, Xianju, Qin, Xuwen
- 出版商: Springer
- 出版日期: 2024-01-05
- 售價: $7,150
- 貴賓價: 9.5 折 $6,793
- 語言: 英文
- 頁數: 235
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9819989965
- ISBN-13: 9789819989966
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相關主題
商品描述
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.
商品描述(中文翻譯)
本書介紹了基於深度學習和遙感技術的地質智能解釋理論和方法。本書的主要研究主題包括岩性和礦物豐度。
本書重點關注以下五個方面：1. 從多層次（像素級、場景級、語義分割級、先驗知識輔助、轉移學習數據集）構建地質遙感數據集，這些數據集是基於深度學習的地質解釋的基礎。2. 基於深度學習、先驗知識和遙感的岩性場景分類研究。3. 基於深度學習和遙感的岩性語義分割研究。4. 基於轉移學習和遙感的岩性分類研究。5. 基於稀疏解混理論和高光譜遙感的礦物豐度反演研究。
本書適合對地質、遙感和人工智慧感興趣的本科生和研究生閱讀，也可作為地質勘探科技人員的參考書籍。
作者簡介
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.
作者簡介(中文翻譯)
陳偉韜博士（IEEE 會員）是中國地質大學（CUG）計算機科學學院的正教授。他於2006年獲得碩士學位，並於2012年獲得博士學位。他在國際期刊上發表了50多篇經過同行評審的技術論文。他的主要研究興趣包括機器學習和地理環境的遙感。陳教授是IEEE的會員，並擔任多個國際期刊的編輯委員和審稿人。他曾獲得2019年土地與資源科學技術進步獎（第二名）和2020年中國指揮與控制學會科學技術獎（第二名）。他還獲得了中國地質大學的「搖籃計畫」人才項目和湖北省武漢市的「晨光計畫」青年科技人才項目。
李先駿博士是中國地質大學（CUG）計算機科學學院的副教授。他於2009年獲得工學學士學位，2012年獲得碩士學位，2016年獲得博士學位，均來自中國地質大學武漢校區。他在地質遙感領域擁有超過十年的機器學習和深度學習技術經驗。他在省級比賽中獲得兩項第二名，並發表了30多篇經過同行評審的論文。
秦旭文博士是中國地質調查局的研究員，曾獲得「李四光學者」稱號。他專注於地質調查中的遙感和物理探測的理論、技術設備及應用，並在多項關鍵技術上取得突破。他在陸地和深海地質災害領域也獲得了許多高水平的成就。相關工作被收錄於中國十大科技發展中，並由中國科學院和中國工程院的成員選出。此外，他在省級比賽中獲得四項第一名和一項第二名，發表了16部專著和43篇SCI學術論文，並獲得12項國際/國內發明專利。
王立哲博士是中國地質大學（CUG）計算機科學學院的正教授。他在中國北京的清華大學獲得工學學士和碩士學位，並在德國卡爾斯魯厄大學獲得工程博士學位（優等）。他是中國地質大學計算機科學學院的楚天講座教授。他的研究興趣包括高性能計算（HPC）、電子科學和遙感影像處理。王教授是IET、IEEE、SPIE和英國計算機學會的會士。他曾獲得2019年土地與資源科學技術進步獎（第二名）和2020年中國指揮與控制學會科學技術獎（第二名）。他還於2019年獲得國家優秀青年科學基金，並於2022年獲得歐洲科學院的獎項。
