nonparametric and probabilistic classification of agricultural crops using multitemporal images...
TRANSCRIPT
Nonparametric and Probabilistic Classification of Agricultural
Crops Using Multitemporal Images
Smögen Workshop, 21-25 August 2006
Jun Yu&
Bo Ranneby
Centre of Biostochastics
The Swedish University of Agricultural Sciences
Umeå, Sweden
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2006-08-22 3
Input Data
• Field Data Block database (marginal part as ground truth) Block database (for evaluation)• Satellite Images
Scene Date 5 scenes 1 scene
SPOT 2 98-10-24 x
Landsat 5 99-05-07 x
Landsat 5 99-07-10 x x
SPOT 4 99-07-30 x
Landsat 7 99-09-11 x
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Crops
25 classes:
Autumn-sown cerealsSpring-sown cerealsSpring-sown oil seed cropsPotatoes……Grass land on arable land (for hay or silage) Energy forest (salix)Wood land on pasture……
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Test sites in the County of Dalarna
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Test sites – background: GSD topographical map
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Methodology
• Define the target function
(in this case, probabilities of correct classification)• Denoise the images• Remove outliers from reference data• Calculate the information values in the components in the
feature vector (e.g. different bands)• Determine a proper metric• Determine prototypes for the classes• Run a nonparametric classification so that the target
function is maximized• Declare the quality of classification result by using
probability matrices
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Classification test site 1
5 scenes 1 scene
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Classification test site 2
5 scenes 1 scene
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Probability Matrices
C1 C2 C3 C4 C5 C6 C7 C8
C1 0,49 0,35 0 0 0,03 0 0,14 0
C2 0,04 0,78 0,01 0 0,03 0 0,14 0,01
C3 0 0,07 0,72 0 0,01 0 0,2 0
C4 0,01 0,09 0,01 0,65 0,04 0 0,2 0
C5 0,02 0,02 0,02 0 0,63 0,01 0,27 0,02
C6 0 0,04 0 0 0,11 0,56 0,27 0,01
C7 0,01 0,04 0 0 0,2 0,01 0,71 0,02
C1 C2 C3 C4 C5 C6 C7 C8
0,19 0,3 0,01 0,03 0,11 0 0,34 0,01
0,04 0,53 0,01 0,01 0,1 0,01 0,27 0,02
0,01 0,07 0,7 0 0,05 0 0,17 0
0,02 0,02 0,01 0,17 0,37 0 0,39 0,01
0,02 0,04 0 0,05 0,47 0,02 0,38 0,02
0,01 0,14 0 0 0,12 0,28 0,42 0,03
0,02 0,06 0,01 0,02 0,28 0,04 0,54 0,03
5 scenes 1 scene
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Probability Matrices at level 1
C1 C2 C3
C1 0,90 0,09 0,01
C2 0,36 0,62 0,02
C1 C2 C3
C1 0,84 0,14 0,02
C2 0,48 0,49 0,03
5 scenes 1 scene
Level 1: C1 – arable land; C2 – pasture and meadows
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More quality …
• Calculate probabilities for classes at pixel level• Calculate entropy for each pixel
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Classification test site 1, 5 scenes
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Probability per class, test site 1, 5 scenes
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Entropy, five scenes, test site 1
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Test site 1
0,00 0,00
0,07
0,64
0,02 0,00 0,00 0,00 0,00
0,16
1,11
0,65
0,74
0,23
0,97
0,82
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Pixel No in row 2
Pro
bab
ilit
y p
er c
lass
(5
sce
nes
)
0,00
0,20
0,40
0,60
0,80
1,00
1,20
En
tro
py
Pixels alongedges etc.
Other crops
Energy forest(salix)
Grass land onarable land (forhay or silage)
Potatoes
Spring-sown oilseed crops
Spring-sowncereals
Autumn-sowncereals
Pixelwise probability per class, and entropy – test site 1
Entropy value
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Entropy, one scene, test site 1
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Classification test site 2, 5 scenes
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Probability per class, test site 2, 5 scenes
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Entropy, five scenes, test site 2
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Test site 2
0,95 0,93
1,38
1,08
1,22
1,301,26
0,050,01 0,01 0,01
0,45
0,37
1,16
0,64
0,21
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Pixel No in row 5
Pro
bab
ilit
y p
er c
lass
(5
scen
es)
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
En
tro
py
Pixels alongedges etc.
Other crops
Energy forest(salix)
Grass land onarable land (forhay or silage)
Potatoes
Spring-sown oilseed crops
Spring-sowncereals
Autumn-sowncereals
Pixelwise probability per class, and entropy – test site 2
Entropy value
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Entropy, one scene, test site 2
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Thank you for your attention!