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.
