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Remote sensing Classification

2. Classification land cover. Image classification uses multispectral digital numbers (colour')Most algorithms are per pixel' classifiers. 3. Classification. 4. Manual interpretation e.g. air photos. Human interpretation / classification relies on attributes such as:Shape, pattern, texture, shadows, size, association, tone, colour.

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Remote sensing Classification

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    1. 1 Remote sensing Classification

    2. 2 Classification – land cover

    3. 3 Classification

    4. 4 Manual interpretation e.g. air photos

    5. 5 Using just one band to classify ?

    6. 6

    7. 7 The role of multispectral sensing in classification multiple bands can be used as input

    8. 8 The role of multispectral sensing in classification

    9. 9 Band / channel selection controls success

    10. 10 sample  band correlation coefficients

    11. 11 Classification: Band / Channel Selection

    12. 12 Two main types of classification Unsupervised: the operator picks the algorithm and number of classes (clusters) … useful for a ‘quickie’ and with little or no ground info Supervised: the operator picks the algorithm and designs the classes based on ground knowledge – takes longer, might be more accurate (!)

    13. 13 A> Unsupervised classification

    14. 14 Unsupervised result – 10 classes (clusters)

    15. 15 B> Supervised classification

    16. 16 Picking training areas – a good sample for each class

    17. 17 Training areas (NASA training website)

    18. 18 Supervised classification

    19. 19 Supervised – class assignment

    20. 20 Supervised classification methods a. Minimum distance (below) b. Parallelepiped (right) c. Maximum likelihood (bottom right)

    21. 21 Supervised classification: how it works

    22. 22 comparison

    23. 23 Relative points for the two methods

    24. 24 End of classification part 1: Lab 3 on Monday part 2: tweaking it out

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