Unsupervised classification (Tutorial)
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:''This article is part of the [[QGIS tutorial 2013/14]].<br/>In this article, you will learn how to classify a landscape raster via k-means clustering'' | :''This article is part of the [[QGIS tutorial 2013/14]].<br/>In this article, you will learn how to classify a landscape raster via k-means clustering'' | ||
| − | # Add the raster layer ''188_pca_indices.tif'' into a [[QGIS]] project. It should be available in the [[Course data|course data]]. | + | # Classifying an image |
| − | # Open the k-means classification algorithm provided by the Orfeo toolbox. It can be found in the processing toolbar under {{mitem|text=Toolbox --> Orfeo Toolbox --> Learning --> Unsupervised KMeans image classification}}. | + | ## Add the raster layer ''188_pca_indices.tif'' into a [[QGIS]] project. It should be available in the [[Course data|course data]]. |
| + | ## Open the k-means classification algorithm provided by the Orfeo toolbox. It can be found in the processing toolbar under {{mitem|text=Toolbox --> Orfeo Toolbox --> Learning --> Unsupervised KMeans image classification}}. | ||
| + | ##* Set the ''188_pca_indices'' layer as {{button|text=Input image}} | ||
| + | ##* Set the {{button|text=Number of classes}} to 20 and the {{button|text=Number of iterations}} to 1000. | ||
| + | ##* The {{button|text=Convergence threshold}} should be set at 0.0001. | ||
| + | ##* Leave all other configurations as they are and click {{button|text=Run}}. The resulting image has 20 classes, labeled from 0 to 19. | ||
| + | # Image symbology | ||
| + | ## Right-click the classified layer in the [[TOC]] and select {{mitem|text=Properties --> Style}}. | ||
| + | ## Set the {{button|text=Render type}} to {{button|text=Singleband pseudocolor}}. | ||
| + | ## Set the {{button|text=Mode}} to {{button|text=Equal interval}} with 20 classes and confirm with {{button|text=Classify}}. | ||
| + | ## In the {{button|text=Load min/max}} section, select tbe {{button|text=Min/max}} radio button and click {{button|text=Load}} to update the range for classification. | ||
| + | ## Confirm with {{button|text=Apply}} or click {{button|text=OK}} if you are content with your settings. | ||
Revision as of 13:35, 26 January 2014
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sorry: |
This section is still under construction! This article was last modified on 01/26/2014. If you have comments please use the Discussion page or contribute to the article! |
- This article is part of the QGIS tutorial 2013/14.
In this article, you will learn how to classify a landscape raster via k-means clustering
- Classifying an image
- Add the raster layer 188_pca_indices.tif into a QGIS project. It should be available in the course data.
- Open the k-means classification algorithm provided by the Orfeo toolbox. It can be found in the processing toolbar under Toolbox --> Orfeo Toolbox --> Learning --> Unsupervised KMeans image classification.
- Set the 188_pca_indices layer as Input image
- Set the Number of classes to 20 and the Number of iterations to 1000.
- The Convergence threshold should be set at 0.0001.
- Leave all other configurations as they are and click Run. The resulting image has 20 classes, labeled from 0 to 19.
- Image symbology
- Right-click the classified layer in the TOC and select Properties --> Style.
- Set the Render type to Singleband pseudocolor.
- Set the Mode to Equal interval with 20 classes and confirm with Classify.
- In the Load min/max section, select tbe Min/max radio button and click Load to update the range for classification.
- Confirm with Apply or click OK if you are content with your settings.
