1 / 11

Landsat unsupervised classification

Landsat unsupervised classification . Zhuosen Wang. 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

gizi
Download Presentation

Landsat unsupervised classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Landsat unsupervised classification Zhuosen Wang

  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

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

  4. 7 classes 5 iteration IsoDATA K-mean P028r035

  5. 7 classes 5 iteration IsoDATA K-mean P028r035

  6. 10 classes 7 classes 5 iterations, IsoDATA

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

  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

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

  10. Harvard Forest

  11. Harvard Forest p012r030

More Related