Remote Sensing Image Classification in R

by Courage Kamusoko

Overview

This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification.

This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification.

R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.

Product Details

ISBN-13:	9789811380129
Publisher:	Springer-Verlag New York, LLC
Publication date:	07/24/2019
Series:	Springer Geography
Sold by:	Barnes & Noble
Format:	eBook
File size:	68 MB
Note:	This product may take a few minutes to download.

About the Author

Courage Kamusoko is a senior researcher at the Asia Air Survey, Japan. His expertise includes land use/cover change modeling, and the design and implementation of geospatial database management systems. His primary research interests are the analysis of remotely sensed images, land use/cover modeling, and machine learning. In addition to his focus on geospatial research and consultancy, he has also taught practical machine learning for geospatial analysis and modeling.

Table of Contents

Dedication.- Preface.- Acknowledgements.- Acronyms and Abbreviations.- Chapter 1. Remote sensing Digital Image Processing in R.- Chapter 2. Pre-processing.- Chapter 3. Image Transformation,etc.

Editorial Reviews

The book provides a hands-on approach to remotely sensed image classification, covering not only classification techniques but also some of the required prior/posterior steps, for example, data preparation, feature extraction, dataset analysis, model tuning, and performance assessment. The book is like a tutorial, with sample code provided throughout the different chapters to offer the reader a practical perspective with R. … This short book remains the first one to address remote sensing image classification in R.” (Sebastien Lefevre,Computing Reviews, July 12, 2021)

From the Publisher