Unsupervised classification (Tutorial)

Revision as of 13:58, 13 February 2014

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This section is still under construction! This article was last modified on 02/13/2014. 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

1. Classifying an image
   1. Add the raster layer 188_pca_indices.tif into a QGIS project. It should be available in the course data.
   2. 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.
2. Image symbology
   1. Right-click the classified layer in the TOC and select Properties --> Style.
   2. Set the Render type to Singleband pseudocolor.
   3. Set the Mode to Equal interval with 20 classes and confirm with Classify.
   4. In the Load min/max section, select tbe Min/max radio button and click Load to update the range for classification.
   5. Confirm with Apply or click OK if you are content with your settings.
3. Create a land use/cover classification scheme table as in table A