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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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