Landsat unsupervised classification Zhuosen Wang 1.

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Unsupervised Classification

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Presentation transcript:

Landsat unsupervised classification Zhuosen Wang 1

Unsupervised classification methods The two most frequently used algorithms K-mean and the ISODATA Minimize the distance between each pixel and its assigned cluster center The ISODATA algorithm allows for different number of clusters while the k-means assumes that the number of clusters is known a priori K-means is very sensitive to initial starting values 2

15 classes, 1 iteration 7 classes, 5 iterations K-mean P028r035 3

7 classes 5 iteration IsoDATA K-mean P028r035 4

7 classes 5 iteration IsoDATA K-mean 5 P028r035

10 classes 7 classes 5 iterations, IsoDATA 6

7 classes, 5 iterations IsoData P12r31 –2011_09_02 7

7 classes, 5 iterations 7 classes, 10 iterations IsoData P12r31 –2011_09_02 No improvement between 10 iterations and 5 iterations Cyan –grass Yellow –deciduous forest blue,green—evergreen forest 8

K-mean IsoDATA 7 classes, 5 iterations P12r31 –2011_09_02

Harvard Forest 10

11 Harvard Forest p012r030