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Remote Sensing Image Classification in R

by Courage Kamusoko
Save 35% Save 35%
Current price ₹10,174.00
Original price ₹15,651.00
Original price ₹15,651.00
Original price ₹15,651.00
(-35%)
₹10,174.00
Current price ₹10,174.00

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Book cover type: Hardcover
  • ISBN13: 9789811380112
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 189
  • Original Price: EUR 139.99
  • Language: English
  • Edition: 2019
  • Item Weight: 468 grams
  • BISAC Subject(s): Remote Sensing & Geographic Information Systems, Earth Sciences / Geography, and Programming / Object Oriented

From the Back Cover

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.

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.

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