Unsupervised classification
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(Created page with "=Unsupervised K-Means classification= * In the search engine of the Processing Toolbox, type {{typed|text=kmeans}} and double click '''KMeansClassification''' of OTB. * Specif...") |
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* In the search engine of the Processing Toolbox, type {{typed|text=kmeans}} and double click '''KMeansClassification''' of OTB. | * In the search engine of the Processing Toolbox, type {{typed|text=kmeans}} and double click '''KMeansClassification''' of OTB. | ||
* Specify a multispectral image as Input Image. | * Specify a multispectral image as Input Image. | ||
| − | * Specify directory and name for the Output image | + | * Specify directory and name for the Output image. |
* Set the {{button|text=Number of classes}} to {{typed|text=20}} | * Set the {{button|text=Number of classes}} to {{typed|text=20}} | ||
* Check the {{button|text=Training set size}} to {{typed|text=10000}} | * Check the {{button|text=Training set size}} to {{typed|text=10000}} | ||
Revision as of 09:25, 4 December 2020
Unsupervised K-Means classification
- In the search engine of the Processing Toolbox, type kmeans and double click KMeansClassification of OTB.
- Specify a multispectral image as Input Image.
- Specify directory and name for the Output image.
- Set the Number of classes to 20
- Check the Training set size to 10000
- Output pixel type: uint8
- Click on Run.
- Load the resulting image into QGIS. It is single band file with 20 grey levels labeled from 0 to 19.
- Layer Properties --> Symbology --> Render type. Switch to Singleband pseudocolor and select a Color ramp (e.g. Spectral). Select the Mode Equal interval and set the number of classes to 20
