Contents
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An overview
This page lists the main topics, followed by a detailed contents page |
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1. Introduction to categorisation of objects from their data
This chapter gives an introduction to the concepts of classification and a short introduction into the basics of visual recognition and sensing |
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2. Introduction into the mathematical methods
The basic mathematical methods like probability, probability density functions like scatterplots |
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3. What is classification?
Here we put all the math together and learn about the basic methods and techniques of classifying remote sensing data |
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4. The Minimum Distance Classifier
A first and basic metrics used for classification |
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5. The Maximum Likelihood Classifier
A second and less simple metrics used for classification |
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6. Errors and Costs in Classification
Is classification a way to automatically adding information? Here we learn about the costs |
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7. Exercises and Tutorials
Additional information to specific topics, exercises and tutorials. |
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8. References
A list of references of literature and data sources as well as suggestions for further reading |
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9. Links
A list of useful external links dealing with the topic (e.g. references, data sources, research projects, case studies) |
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10. Image Credits
A list of all images used in the tutorial with indications of their sources |
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11. Authors
A list of the authors who created the tutorial |
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12. Exercises, Answers and Tutorials
Additional information to specific topics, exercises, answers and tutorials. (This link is only visible for teachers) |
