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Landsat unsupervised classification

Zhuosen Wang

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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

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

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7 classes 5 iteration IsoDATA K-mean P028r035

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7 classes 5 iteration IsoDATA K-mean

P028r035

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10 classes 7 classes 5 iterations, IsoDATA

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7 classes, 5 iterations IsoDataP12r31 –2011_09_02

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

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

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Harvard Forest

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Harvard Forest p012r030