東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Cities and towns- Remote sensing." ]

切換: 標籤 | MARC模式 | ISBD

Optical and SAR remote sensing of ur...

Kamusoko, Courage.

FindBook

Google Book

Amazon

博客來

Optical and SAR remote sensing of urban areas = a practical guide /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optical and SAR remote sensing of urban areas/ by Courage Kamusoko.
其他題名:	a practical guide /
作者:	Kamusoko, Courage.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	xi, 119 p. :ill., digital ;24 cm.
內容註:	- Geospatial Machine Learning in Urban Areas: Challenges and Prospects -- Exploratory Analysis and Transformation for Remotely-Sensed Imagery -- Mapping Urban Land Cover using Multi-seasonal Sentinel-2 Imagery, Spectral and Texture Indices -- Mapping Urban Land Cover using Multi-seasonal Sentinel-1 Imagery and Texture Indices -- Improving Urban Land Cover Mapping -- Land Cover Classification Accuracy Assessment -- Appendix.
Contained By:	Springer Nature eBook
標題:	Cities and towns - Remote sensing. -
電子資源:	https://doi.org/10.1007/978-981-16-5149-6
ISBN:	9789811651496

Optical and SAR remote sensing of urban areas = a practical guide /
Kamusoko, Courage.

Optical and SAR remote sensing of urban areasa practical guide /[electronic resource] :by Courage Kamusoko. - Singapore :Springer Singapore :2022. - xi, 119 p. :ill., digital ;24 cm. - Springer geography,2194-3168. - Springer geography..

- Geospatial Machine Learning in Urban Areas: Challenges and Prospects -- Exploratory Analysis and Transformation for Remotely-Sensed Imagery -- Mapping Urban Land Cover using Multi-seasonal Sentinel-2 Imagery, Spectral and Texture Indices -- Mapping Urban Land Cover using Multi-seasonal Sentinel-1 Imagery and Texture Indices -- Improving Urban Land Cover Mapping -- Land Cover Classification Accuracy Assessment -- Appendix.

This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data. There are many introductory and advanced books on optical and SAR remote sensing image processing, but most of them do not serve as good practical guides. However, this book is designed as a practical guide and a hands-on workbook, where users can explore data and methods to improve their land cover mapping skills for urban areas. Although there are many freely available earth observation data, the focus is on land cover mapping using Sentinel-1 C-band SAR and Sentinel-2 data. All remotely sensed image processing and classification procedures are based on open-source software applications such QGIS and R as well as cloud-based platforms such as Google Earth Engine (GEE) The book is organized into six chapters. Chapter 1 introduces geospatial machine learning, and Chapter 2 covers exploratory image analysis and transformation. Chapters 3 and 4 focus on mapping urban land cover using multi-seasonal Sentinel-2 imagery and multi-seasonal Sentinel-1 imagery, respectively. Chapter 5 discusses mapping urban land cover using multi-seasonal Sentinel-1 and Sentinel-2 imagery as well as other derived data such as spectral and texture indices. Chapter 6 concludes the book with land cover classification accuracy assessment.

ISBN: 9789811651496

Standard No.: 10.1007/978-981-16-5149-6doiSubjects--Topical Terms:

3444196
Cities and towns
--Remote sensing.

LC Class. No.: G70.4 / .K35 2022

Dewey Class. No.: 307.760285

Optical and SAR remote sensing of urban areas = a practical guide /
LDR:02957nmm a2200337 a 4500 001 2295704
003 DE-He213
005 20211201233811.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811651496 $q (electronic bk.)
020 $a 9789811651489 $q (paper)
024 7 $a 10.1007/978-981-16-5149-6 $2 doi
035 $a 978-981-16-5149-6
040 $a GP $c GP
041 0 $a eng
050 4 $a G70.4 $b .K35 2022
072 7 $a UB $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UB $2 thema
082 0 4 $a 307.760285 $2 23
090 $a G70.4 $b .K15 2022
100 1 $a Kamusoko, Courage. $3 3589779
245 1 0 $a Optical and SAR remote sensing of urban areas $h [electronic resource] : $b a practical guide / $c by Courage Kamusoko.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xi, 119 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer geography, $x 2194-3168
505 0 $a - Geospatial Machine Learning in Urban Areas: Challenges and Prospects -- Exploratory Analysis and Transformation for Remotely-Sensed Imagery -- Mapping Urban Land Cover using Multi-seasonal Sentinel-2 Imagery, Spectral and Texture Indices -- Mapping Urban Land Cover using Multi-seasonal Sentinel-1 Imagery and Texture Indices -- Improving Urban Land Cover Mapping -- Land Cover Classification Accuracy Assessment -- Appendix.
520 $a This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data. There are many introductory and advanced books on optical and SAR remote sensing image processing, but most of them do not serve as good practical guides. However, this book is designed as a practical guide and a hands-on workbook, where users can explore data and methods to improve their land cover mapping skills for urban areas. Although there are many freely available earth observation data, the focus is on land cover mapping using Sentinel-1 C-band SAR and Sentinel-2 data. All remotely sensed image processing and classification procedures are based on open-source software applications such QGIS and R as well as cloud-based platforms such as Google Earth Engine (GEE) The book is organized into six chapters. Chapter 1 introduces geospatial machine learning, and Chapter 2 covers exploratory image analysis and transformation. Chapters 3 and 4 focus on mapping urban land cover using multi-seasonal Sentinel-2 imagery and multi-seasonal Sentinel-1 imagery, respectively. Chapter 5 discusses mapping urban land cover using multi-seasonal Sentinel-1 and Sentinel-2 imagery as well as other derived data such as spectral and texture indices. Chapter 6 concludes the book with land cover classification accuracy assessment.
650 0 $a Cities and towns $x Remote sensing. $3 3444196
650 0 $a Surveying. $3 545648
650 1 4 $a Computer Applications. $3 891249
650 2 4 $a Geography, general. $3 2162156
650 2 4 $a Human Geography. $3 896266
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer geography. $3 1567644
856 4 0 $u https://doi.org/10.1007/978-981-16-5149-6
950 $a Earth and Environmental Science (SpringerNature-11646)