nonparametric and probabilistic classification of agricultural crops using multitemporal images...
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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
2006-08-22 2
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
2006-08-22 4
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……
2006-08-22 5
Test sites in the County of Dalarna
2006-08-22 6
Test sites – background: GSD topographical map
2006-08-22 7
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
2006-08-22 8
Classification test site 1
5 scenes 1 scene
2006-08-22 9
Classification test site 2
5 scenes 1 scene
2006-08-22 10
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
2006-08-22 11
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
2006-08-22 12
More quality …
• Calculate probabilities for classes at pixel level• Calculate entropy for each pixel
2006-08-22 13
Classification test site 1, 5 scenes
2006-08-22 14
Probability per class, test site 1, 5 scenes
2006-08-22 15
Entropy, five scenes, test site 1
2006-08-22 16
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
2006-08-22 17
Entropy, one scene, test site 1
2006-08-22 18
Classification test site 2, 5 scenes
2006-08-22 19
Probability per class, test site 2, 5 scenes
2006-08-22 20
Entropy, five scenes, test site 2
2006-08-22 21
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
2006-08-22 22
Entropy, one scene, test site 2
2006-08-22 23
Thank you for your attention!
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