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Title
Remote sensing intelligent interpretation for mine geological environment : from land use and land cover perspective / Weitao Chen, Xianju Li, Lizhe Wang.
ISBN
9789811937392 (electronic bk.)
9811937397 (electronic bk.)
9789811937385
9811937389
Published
Singapore : Springer, 2022.
Language
English
Description
1 online resource (1 volume) : illustrations (black and white).
Item Number
10.1007/978-981-19-3739-2 doi
Call Number
TD195.M5
Dewey Decimal Classification
338.20285
Summary
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 detectionscene classificationsemantic segmentation." Taking Chinas 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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Preface.-Mine geological environment: An overview.-Multimodal remote sensing science and technology.-Deep learning technology for remote sensing intelligent interpretation.-Remote sensing interpretation signs of mine land occupation type
Mine remote sensing dataset construction for multi-level tasks
Mine target detection by remote sensing and deep learning
Mine remote sensing scene classification by deep learning
Mine land occupation classification based on machine learning and remote sensing images
Mine land occupation classification based on deep learning and remote sensing images
Concluding remarks.
