Unsupervised classification (Tutorial)
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## 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. | ## 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. | ## 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''' | ||
| + | |||
| + | {| class='wikitable' | ||
| + | |- | ||
| + | |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 | ||
| + | |} | ||
Revision as of 13:53, 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
- 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
| 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 |
