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Jun Yu & Bo Ranneby Centre of Biostochastics The Swedish University of Agricultural Sciences

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. Input Data. Field Data

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Jun Yu & Bo Ranneby Centre of Biostochastics The Swedish University of Agricultural Sciences

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  1. Nonparametric and Probabilistic Classification of Agricultural Crops Using Multitemporal ImagesSmögen Workshop, 21-25 August 2006 Jun Yu & Bo Ranneby Centre of Biostochastics The Swedish University of Agricultural Sciences Umeå, Sweden

  2. Input Data • Field Data • Block database (marginal part as ground truth) • Block database (for evaluation) • Satellite Images

  3. 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 ……

  4. Test sites in the County of Dalarna

  5. Test sites – background: GSD topographical map

  6. 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

  7. Classification test site 1 5 scenes 1 scene

  8. Classification test site 2 1 scene 5 scenes

  9. Probability Matrices 5 scenes 1 scene

  10. Probability Matrices at level 1 5 scenes 1 scene Level 1: C1 – arable land; C2 – pasture and meadows

  11. More quality … • Calculate probabilities for classes at pixel level • Calculate entropy for each pixel

  12. Classification test site 1, 5 scenes

  13. Probability per class, test site 1, 5 scenes

  14. Entropy, five scenes, test site 1

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

  16. Entropy, one scene, test site 1

  17. Classification test site 2, 5 scenes

  18. Probability per class, test site 2, 5 scenes

  19. Entropy, five scenes, test site 2

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

  21. Entropy, one scene, test site 2

  22. Thank you for your attention!

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