語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Remote Sensing Intelligent Interpretation for Mine Geological Environment = From Land Use and Land Cover Perspective /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Remote Sensing Intelligent Interpretation for Mine Geological Environment/ by Weitao Chen, Xianju Li, Lizhe Wang.
其他題名:
From Land Use and Land Cover Perspective /
作者:
Chen, Weitao.
其他作者:
Wang, Lizhe.
面頁冊數:
XII, 246 p. 110 illus., 89 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Environmental Monitoring. -
電子資源:
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 Environment
From Land Use and Land Cover Perspective /[electronic resource] :by Weitao Chen, Xianju Li, Lizhe Wang. - 1st ed. 2022. - XII, 246 p. 110 illus., 89 illus. in color.online resource.
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:
894984
Environmental Monitoring.
LC Class. No.: G70.212-.217
Dewey Class. No.: 910.285
Remote Sensing Intelligent Interpretation for Mine Geological Environment = From Land Use and Land Cover Perspective /
LDR
:03325nam a22003975i 4500
001
1082152
003
DE-He213
005
20220819232513.0
007
cr nn 008mamaa
008
221228s2022 si | s |||| 0|eng d
020
$a
9789811937392
$9
978-981-19-3739-2
024
7
$a
10.1007/978-981-19-3739-2
$2
doi
035
$a
978-981-19-3739-2
050
4
$a
G70.212-.217
072
7
$a
RGW
$2
bicssc
072
7
$a
COM018000
$2
bisacsh
072
7
$a
RGW
$2
thema
082
0 4
$a
910.285
$2
23
100
1
$a
Chen, Weitao.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1387717
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.
250
$a
1st ed. 2022.
264
1
$a
Singapore :
$b
Springer Nature Singapore :
$b
Imprint: Springer,
$c
2022.
300
$a
XII, 246 p. 110 illus., 89 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
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.
650
2 4
$a
Environmental Monitoring.
$3
894984
650
2 4
$a
Signal, Speech and Image Processing .
$3
1366353
650
2 4
$a
Machine Learning.
$3
1137723
650
1 4
$a
Geographical Information System.
$3
1365742
650
0
$a
Environmental monitoring.
$3
555916
650
0
$a
Geology.
$3
670379
650
0
$a
Signal processing.
$3
561459
650
0
$a
Machine learning.
$3
561253
650
0
$a
Geographic information systems.
$3
554796
700
1
$a
Wang, Lizhe.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
787843
700
1
$a
Li, Xianju.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1387718
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811937385
776
0 8
$i
Printed edition:
$z
9789811937408
776
0 8
$i
Printed edition:
$z
9789811937415
856
4 0
$u
https://doi.org/10.1007/978-981-19-3739-2
912
$a
ZDB-2-EES
912
$a
ZDB-2-SXEE
950
$a
Earth and Environmental Science (SpringerNature-11646)
950
$a
Earth and Environmental Science (R0) (SpringerNature-43711)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入