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http://github.com/tkorting/youtube/

Individual decision trees can be used to retrieve class uncertainty

shadow Vs water

| case | cloud | free | shadow | water | entropy |

| 1 | 100 | 0 | 0 | 0 | 0.000 |

| 2 | 0 | 0 | 25 | 75 | 0.562 |

| 3 | 0 | 50 | 0 | 50 | 0.693 |

| 4 | 25 | 25 | 0 | 50 | 1.039 |

  • LaTeX equations
  • Algorithms source codes
  • Datasets used in videos

transition cloud-free

Random Forest for image classification

http://youtube.com/tkorting

Thales Sehn Körting

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Class prediction using Random Forests

Random Forests for image classification

1

[[ 54 49 49 48 48 48 48 48 51 51]

[ 45 46 46 43 43 45 45 45 44 44]

[ 45 46 46 43 43 45 45 45 44 44]

[ 61 90 90 82 82 48 42 42 47 47]

[138 139 139 122 122 78 47 47 46 46]

[138 139 139 122 122 78 47 47 46 46]

[144 144 144 140 140 124 70 70 48 48]

[144 144 144 140 140 124 70 70 48 48]

[146 144 144 142 142 145 80 80 51 51]

[146 144 144 142 142 145 80 80 51 51]]

[[ 22 20 20 19 19 19 19 19 20 20]

[ 9 11 11 7 7 16 16 16 17 17]

[ 9 11 11 7 7 16 16 16 17 17]

[ 25 54 54 46 46 20 14 14 20 20]

[ 86 90 90 74 74 41 10 10 22 22]

[ 86 90 90 74 74 41 10 10 22 22]

[ 92 94 94 90 90 86 33 33 25 25]

[ 92 94 94 90 90 86 33 33 25 25]

[ 93 93 93 91 91 108 43 43 22 22]

[ 93 93 93 91 91 108 43 43 22 22]]

Sample 1 - bare soil [94, 144, 206]

Sample 2 - vegetation [16, 45, 21]

[[ 20 23 23 22 22 20 20 20 21 21]

[ 40 41 41 37 37 21 21 21 18 18]

[ 40 41 41 37 37 21 21 21 18 18]

[ 60 96 96 86 86 24 19 19 21 21]

[210 199 199 173 173 74 38 38 22 22]

[210 199 199 173 173 74 38 38 22 22]

[220 206 206 200 200 129 65 65 24 24]

[220 206 206 200 200 129 65 65 24 24]

[220 211 211 207 207 167 84 84 25 25]

[220 211 211 207 207 167 84 84 25 25]]

Sample N - class name ...

1

in this example:

- 4 input features

- 4 classes (~31000 samples)

  • 8000 cloud
  • 8000 free
  • ~7000 shadow
  • 8000 water

- 4 trees (in the forest)

input

- blue

- green

- red

- nir

output:

- cloud

- free

- shadow

- water

2

3

Majority vote = cloud (3 votes)

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