Unsupervised classification (Tutorial)
From AWF-Wiki
(Difference between revisions)
| Line 2: | Line 2: | ||
:''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'' | ||
| − | + | {| class='wikitable floatright' | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | {| class='wikitable' | + | |
|+Table A | |+Table A | ||
|- | |- | ||
| Line 67: | Line 45: | ||
|255-255-255 | |255-255-255 | ||
|} | |} | ||
| + | |||
| + | # Classifying an image | ||
| + | ## 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. | ||
| + | # Create a land use/cover classification scheme table as in table '''A''' | ||
| + | # As a reference, you may add a google layer to the project:<br/> {{mitem|text=Plugins --> ObenLayers plugin --> Add Google Satellite layer}} | ||
| + | # Set the coordinate reference system | ||
| + | ## Open {{mitem|text=Project --> Project properties}}. | ||
| + | ## Set {{button|text=WGS 84 Pseudo Mercarot}} ({{button|text=EPSG 3857}}) as coordinate system | ||
| + | ## Check the {{button|text=Enable on the fly transformation}} box | ||
| + | ## Right click {{button|text=188_pca_indices}} in the [[TOC]] and select {{button|text=Set Project CRS from layer}}. | ||
Revision as of 13:04, 13 February 2014
 |
sorry: |
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
| 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 |
- 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.
- Create a land use/cover classification scheme table as in table A
- As a reference, you may add a google layer to the project:
Plugins --> ObenLayers plugin --> Add Google Satellite layer - Set the coordinate reference system
- Open Project --> Project properties.
- Set WGS 84 Pseudo Mercarot (EPSG 3857) as coordinate system
- Check the Enable on the fly transformation box
- Right click 188_pca_indices in the TOC and select Set Project CRS from layer.
