https://github.com/ocsmit/alcc

Rough implementation of the Automated landcover classification using unsupervised classification methods.
https://github.com/ocsmit/alcc

gis landcover-classification remote-sensing

Last synced: about 1 month ago
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Rough implementation of the Automated landcover classification using unsupervised classification methods.

• Host: GitHub
• URL: https://github.com/ocsmit/alcc
• Owner: ocsmit
• License: other
• Created: 2020-02-16T02:18:53.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-17T15:32:49.000Z (about 5 years ago)
• Last Synced: 2024-11-13T09:48:39.682Z (3 months ago)
• Topics: gis, landcover-classification, remote-sensing
• Language: Python
• Homepage:
• Size: 44.9 KB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
### Automated Landcover Classification using unsupervised classification methods

**Author: Owen Smith, IESA, University of North Georgia**

#### `alcc_arcpy(landsat_dir, out_dir, soil_brightness):` Runtime: ~2:15 minutes

* `landsat_dir 'str':` Input landsat data directory.

* `out_dir 'str':` Directory where all outputs will be saved.

* `soil_brightness=0.5 'int':` Soil brightness factor for SAVI calculation.

Final output `out_dir/ALCC.tif`

Classification values still need tweaked.

Plans to implement scikit learn clustering with numpy arrays to replace arcgis unsupervised isocluster.

#### `alcc_foss:`

* WIP

Citations: 

- Gašparović, M., Zrinjski, M., & Gudelj, M. (2019). Automatic cost-effective 

  method for land cover classification (ALCC). Computers, Environment and Urban 

  Systems, 76, 1-10.