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Nonparametric and Probabilistic Classification of Agricultural Crops Using Multitemporal Images Smögen Workshop, 21-25 August 2006 Jun Yu & Bo Ranneby.

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Presentation on theme: "Nonparametric and Probabilistic Classification of Agricultural Crops Using Multitemporal Images Smögen Workshop, 21-25 August 2006 Jun Yu & Bo Ranneby."— Presentation transcript:

1 Nonparametric and Probabilistic Classification of Agricultural Crops Using Multitemporal Images Smögen Workshop, August 2006 Jun Yu & Bo Ranneby Centre of Biostochastics The Swedish University of Agricultural Sciences Umeå, Sweden

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3 3 Input Data Field Data Block database (marginal part as ground truth) Block database (for evaluation) Satellite Images SceneDate5 scenes1 scene SPOT x Landsat x Landsat x x SPOT x Landsat x

4 Crops 25 classes: Autumn-sown cereals Spring-sown cereals Spring-sown oil seed crops Potatoes …… Grass land on arable land (for hay or silage) Energy forest (salix) Wood land on pasture ……

5 Test sites in the County of Dalarna

6 Test sites – background: GSD topographical map

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

8 Classification test site 1 5 scenes1 scene

9 Classification test site 2 5 scenes 1 scene

10 Probability Matrices C1C2C3C4C5C6C7C8 C10,490,35000,0300,140 C20,040,780,0100,0300,140,01 C300,070,7200,0100,20 C40,010,090,010,650,0400,20 C50,02 00,630,010,270,02 C600,04000,110,560,270,01 C70,010,04000,20,010,710,02 C1C2C3C4C5C6C7C8 0,190,30,010,030,1100,340,01 0,040,530,01 0,10,010,270,02 0,010,070,700,0500,170 0,02 0,010,170,3700,390,01 0,020,0400,050,470,020,380,02 0,010,14000,120,280,420,03 0,020,060,010,020,280,040,540,03 5 scenes1 scene

11 Probability Matrices at level 1 C1C2C3 C10,900,090,01 C20,360,620,02 C1C2C3 C10,840,140,02 C20,480,490,03 5 scenes1 scene Level 1: C1 – arable land; C2 – pasture and meadows

12 More quality … Calculate probabilities for classes at pixel level Calculate entropy for each pixel

13 Classification test site 1, 5 scenes

14 Probability per class, test site 1, 5 scenes

15 Entropy, five scenes, test site 1

16 Pixelwise probability per class, and entropy – test site 1 Entropy value

17 Entropy, one scene, test site 1

18 Classification test site 2, 5 scenes

19 Probability per class, test site 2, 5 scenes

20 Entropy, five scenes, test site 2

21 Pixelwise probability per class, and entropy – test site 2 Entropy value

22 Entropy, one scene, test site 2

23 Thank you for your attention!


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