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Optical and SAR remote sensing of urban areas [electronic resource] : a practical guide / Courage Kamusoko.

Kamusoko, Courage.
2022
G70.4
Available Online
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Title
Optical and SAR remote sensing of urban areas [electronic resource] : a practical guide / Courage Kamusoko.
Author
Kamusoko, Courage.
ISBN
9789811651496 (electronic bk.)
9811651493 (electronic bk.)
9811651485
9789811651489
DOI
https://doi.org/10.1007/978-981-16-5149-6
Published
Singapore : Springer, [2022]
Language
English
Description
1 online resource.
Item Number
10.1007/978-981-16-5149-6 doi
Call Number
G70.4
Dewey Decimal Classification
307.760285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 14, 2021).
Series
Springer geography.
Available in Other Form
Print version: 9789811651489
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Online Access
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All Resources
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.

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