1. Landsat unsupervised classification Zhuosen Wang 1

2. 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

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

4. 7 classes 5 iteration IsoDATA K-mean P028r035 4

5. 7 classes 5 iteration IsoDATA K-mean 5 P028r035

6. 10 classes 7 classes 5 iterations, IsoDATA 6

7. 7 classes, 5 iterations IsoData P12r31 –2011_09_02 7

8. 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

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

10. Harvard Forest 10

11. 11 Harvard Forest p012r030