Unsupervised classification (Tutorial)

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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

Table A

Code	Name	Cluster number	RGB color
1	Urban area	10,9,15	230-000-077
2	Cropland	16,0,13,3,7,17,18	255-255-268
3	Pastures/grassland	12,8	230-230-077
4	Broadleave forest	6,2,5,4	128-255-000
5	Coniferous forest	11,19	000-166-000
6	Water bodies	14	128-242-230
7	Cloud	1	255-255-255

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
4. As a reference, you may add a google layer to the project:
   Plugins --> ObenLayers plugin --> Add Google Satellite layer
5. Set the coordinate reference system
   1. Open Project --> Project properties.
   2. Set WGS 84 Pseudo Mercarot (EPSG 3857) as coordinate system
   3. Check the Enable on the fly transformation box
   4. Right click 188_pca_indices in the TOC and select Set Project CRS from layer.