This content presents how to classify satellite image by QGIS Semi-automatic classification plugin. It includes pre-processing, create a region of interest (AOI), and applying classification methods.

1. Center for Research and Application for Satellite Remote Sensing
Yamaguchi University
Image Classification

2. • The Semi-Automatic Classification Plugin (SCP) is a free open source
plugin for QGIS that allows for the semi-automatic classification (also
supervised and unsupervised classification) of remote sensing images.
Also, it provides several tools for the download of free images (Landsat,
Sentinel-2, Sentinel-3, ASTER, MODIS), the preprocessing of images, the
postprocessing of classifications, and the raster calculation.
Congedo Luca (2016). Semi-Automatic Classification Plugin Documentation.
DOI: http://dx.doi.org/10.13140/RG.2.2.29474.02242/1
Semi-Automatic Classification Plugin

3. Video: https://www.youtube.com/user/fromgistors/videos
Learning source from SCP developer

4. http://www.gisagmaps.com/landsat-8-sentinel-2-bands/
Landsat-8 Sentinel-2
Layer 1
Layer 2
Layer 3
Layer 4
Layer 1
Layer 2
Layer 3
Layer 4
Merged image Merged image
Satellite sensor information

5. • “Plugins” → “manage and install plugin” → search for “semi-automatic
classification PI” -> install
Semi-automatic classification

6. Semi-automatic classification plugin (SCP)

7. Pre-processing Training Input Classification
SCP: 3 Steps

8. • Go to icon of semi-automatic classification plugin → Preprocessing
• Or directly to preprocessing tool
Input
Run → select
directory
to save
Import image and pre-processing

9. • In band set will show image from preprocessing → click “RUN”
In band set will show image from preprocessing

10. RGB tool in semi-classification plugin

11. RGB tool in semi-classification plugin

12. Classes (MC ID)
• 1 Water
• 2 Tree and high vegetation
• 3 Low vegetation, grassland
• 4 Building, manmade
• 5 Openland, bareland
• # you can decide your own classes
• In this workshop, we will make just MC class
Optional → you can make sub-classes
SCP allows for the definition of Macroclass ID (i.e. MC
ID) and Class ID (i.e. C ID), which are the identification
codes of land cover classes. A Macroclass is a group of
ROIs having different Class ID, which is useful when one
needs to classify materials that have different spectral
signatures in the same land cover class.
Create ROI

13. Create training input .scp

14. Create training or ROI

15. Create training or ROI

16. Change color for MC ID (in Macroclass list)

17. Change color

18. Optional!!! if you want to check
and manage your training area.
Page 15 - 25

19. Merge highlighted
spectral signature
Merge signature

20. Delete highlighted
spectral signature
Delete signature

21. Merged spectral signature: 5 classes

22. Add highlighted
spectral signature to
spectral signature plot
Add highlighted item
to scatter plot
Selected or highlighted signature list

23. Scatter plot between band 1 and band 2

24. Scatter plot between band 1 and band 3

25. All pixels Training data
Scatter plot between band 3 and band 4

26. Spectral Signature Plot: Plot

27. Spectral Signature Plot: Signature Detail

28. Training area can be exported

29. IMAGE CLASSIFICATION

30. There are 3 classification algorithm for this
plugin
1) Minimum distance
2) Maximum Likelihood
3) Spectral Angle Mapping
https://fromgistors.blogspot.com/p/user-manual.html
Select classification method

31. See your classification result

35. Run

38. Classification Report

39. https://fromgistors.blogspot.com/p/semi-automatic-classification-plugin.html
Accuracy assessment
• https://www.youtube.com/watch?v=MxBwMQnyZKw
Reference
Congedo Luca (2016). Semi-Automatic Classification Plugin Documentation.
DOI: http://dx.doi.org/10.13140/RG.2.2.29474.02242/1