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

## 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:

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

2006-08-222

3 Input Data Field Data Block database (marginal part as ground truth) Block database (for evaluation) Satellite Images SceneDate5 scenes1 scene SPOT 298-10-24 x Landsat 599-05-07 x Landsat 599-07-10 x x SPOT 499-07-30 x Landsat 799-09-11 x

2006-08-224 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 ……

2006-08-225 Test sites in the County of Dalarna

2006-08-226 Test sites – background: GSD topographical map

2006-08-227 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

2006-08-228 Classification test site 1 5 scenes1 scene

2006-08-229 Classification test site 2 5 scenes 1 scene

2006-08-2210 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

2006-08-2211 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

2006-08-2212 More quality … Calculate probabilities for classes at pixel level Calculate entropy for each pixel

2006-08-2213 Classification test site 1, 5 scenes

2006-08-2214 Probability per class, test site 1, 5 scenes

2006-08-2215 Entropy, five scenes, test site 1

2006-08-2216 Pixelwise probability per class, and entropy – test site 1 Entropy value

2006-08-2217 Entropy, one scene, test site 1

2006-08-2218 Classification test site 2, 5 scenes

2006-08-2219 Probability per class, test site 2, 5 scenes

2006-08-2220 Entropy, five scenes, test site 2

2006-08-2221 Pixelwise probability per class, and entropy – test site 2 Entropy value

2006-08-2222 Entropy, one scene, test site 2

2006-08-2223 Thank you for your attention!

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