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Remote sensing intelligent interpret...

Chen, Weitao.

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Remote sensing intelligent interpretation for mine geological environment = from land use and land cover perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote sensing intelligent interpretation for mine geological environment/ by Weitao Chen, Xianju Li, Lizhe Wang.
Reminder of title:	from land use and land cover perspective /
Author:	Chen, Weitao.
other author:	Li, Xianju.
Published:	Singapore :Springer Nature Singapore : : 2022.,
Description:	xii, 246 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-981-19-3739-2
ISBN:	9789811937392

Remote sensing intelligent interpretation for mine geological environment = from land use and land cover perspective /
Chen, Weitao.

Remote sensing intelligent interpretation for mine geological environmentfrom land use and land cover perspective /[electronic resource] :by Weitao Chen, Xianju Li, Lizhe Wang. - Singapore :Springer Nature Singapore :2022. - xii, 246 p. :ill., digital ;24 cm.

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.

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 China's 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.

ISBN: 9789811937392

Standard No.: 10.1007/978-981-19-3739-2doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: TD195.M5 / C44 2022

Dewey Class. No.: 338.20285

Remote sensing intelligent interpretation for mine geological environment = from land use and land cover perspective /
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245 1 0 $a Remote sensing intelligent interpretation for mine geological environment $h [electronic resource] : $b from land use and land cover perspective / $c by Weitao Chen, Xianju Li, Lizhe Wang.
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505 0 $a 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.
520 $a 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 China's 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.
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650 0 $a Mineral industries $x Environmental aspects $x Remote sensing. $3 3604147
650 0 $a Environmental impact analysis. $3 543037
700 1 $a Li, Xianju. $3 3604145
700 1 $a Wang, Lizhe. $3 3604146
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