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Remote Sensing Image Classification in R

  • Courage┬áKamusoko
Book

Part of the Springer Geography book series (SPRINGERGEOGR)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Courage Kamusoko
    Pages 1-24
  3. Courage Kamusoko
    Pages 25-66
  4. Courage Kamusoko
    Pages 67-79
  5. Courage Kamusoko
    Pages 81-153
  6. Courage Kamusoko
    Pages 155-181
  7. Back Matter
    Pages 183-189

About this book

Introduction

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.

Keywords

Remotely-Sensed Image Classification Image Classification with Machine Learning Single-Date Image Classification Multi-Date Image Classification High Data Dimensionality Open Source Software R

Authors and affiliations

  • Courage┬áKamusoko
    • 1
  1. 1.Asia Air Survey Co., Ltd.KawasakiJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-8012-9
  • Copyright Information Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-981-13-8011-2
  • Online ISBN 978-981-13-8012-9
  • Series Print ISSN 2194-315X
  • Series Online ISSN 2194-3168
  • Buy this book on publisher's site
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