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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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!