Unsupervised classification (Tutorial)

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This section is still under construction! This article was last modified on 02/17/2014. 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.
  6. Change the grid values
    1. Select Saga --> Grid-Tools --> Change grid values from the Processing toolbox.
    2. In the appearing menu, select the grid and Replace condition (I'm not shure if this is correct - Levent (talk) 08:41, 17 February 2014 (CET))
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