Remote Sensing Image Classification in R

Preface
	Who should use this workbook?
	How is this workbook organized?
	Conventions used in this workbook
	Data sets, R scripts, and online resources
Acknowledgements
Contents
Abbreviations and Acronyms
1 Remote Sensing Digital Image Processing in R
	Abstract
	1.1 Introduction
		1.1.1 Remote Sensing Digital Image Processing
		1.1.2 Overview of Machine Learning
	1.2 Overview of R
		1.2.1 What Is R?
		1.2.2 Installing R
		1.2.3 RStudio
		1.2.4 R Packages
		1.2.5 Overview of Data Type, Structure, and Functions
		1.2.6 Getting Help
		1.2.7 Handling Errors and Warnings
		1.2.8 Other Issues
	1.3 Data and Test Site
		1.3.1 Landsat Imagery
		1.3.2 Reference Data Sets
		1.3.3 Overview of Harare Metropolitan Province
	1.4 Tutorial 1: A Quick Guide to R
		1.4.1 Objectives
		1.4.2 Overview of the Packages
		1.4.3 R Basics
		1.4.4 Procedure
	1.5 Summary
	1.6 Additional Exercises
	References
2 Pre-processing
	Abstract
	2.1 Background
		2.1.1 Radiometric Correction
	2.2 Tutorial 1: Display Landsat 5 TM Imagery
		2.2.1 Objectives
		2.2.2 Overview of Packages
		2.2.3 Procedure
	2.3 Tutorial 2: Radiometric Correction and Reprojection
		2.3.1 Objectives
		2.3.2 Procedure
	2.4 Summary
	2.5 Additional Exercises
	References
3 Image Transformation
	Abstract
	3.1 Background
		3.1.1 Spectral Indices
		3.1.2 Texture Indices
	3.2 Tutorial 1: Vegetation Indices
		3.2.1 Objective
		3.2.2 Overview of the Packages
		3.2.3 Procedure
	3.3 Tutorial 2: Texture Analysis
		3.3.1 Objective
		3.3.2 Overview of the Packages
		3.3.3 Procedure
	3.4 Summary
	3.5 Additional Exercises
	References
4 Image Classification
	Abstract
	4.1 Overview of Image Classification
		4.1.1 k-Nearest Neighbors (KNN)
		4.1.2 Artificial Neural Networks (ANN)
		4.1.3 Single Decision Trees (DT)
		4.1.4 Support Vector Machines (SVM)
		4.1.5 Random Forest (RF)
	4.2 Tutorial 1: Single Date Image Classification
		4.2.1 Objectives
		4.2.2 Overview of Packages
		4.2.3 Procedure
		4.2.4 Summary for Tutorial 1
	4.3 Tutorial 2: Multidate Landsat Image Classification
		4.3.1 Objective
		4.3.2 Procedure
		4.3.3 Summary for Tutorial 2
	4.4 Summary
	4.5 Additional Exercises
	References
5 Improving Image Classification
	Abstract
	5.1 Overview
	5.2 Feature Selection
		5.2.1 Importance of Feature Selection
		5.2.2 Recursive Feature Elimination (RFE)
	5.3 Tutorial 1: Image Classification Using Multiple Data Sets
		5.3.1 Objective
		5.3.2 Procedure
		5.3.3 Summary for Tutorial 1
	5.4 Tutorial 2: Image Classification Using Multiple Data Sets with Feature Selection
		5.4.1 Objective
		5.4.2 Procedure
		5.4.3 Summary for Tutorial 2
	5.5 Summary
	5.6 Additional Exercises
	References
Appendix
Index