This is a basic tutorial about the use of Semi-Automatic Classification Plugin (SCP) for the unsupervised classification of a multispectral image. It is recommended to read the Brief Introduction to Remote Sensing before this tutorial, and in particular the part Clustering.
Clustering can be used for unsupervised classification, which means that no training input is required, producing classes (i.e. clusters) that have no definition and consequently the user must assign a land cover label to each class.
The purpose of the classification is to identify the land cover classes with the corresponding ID codes defined in the following table.
Classes
| Class name | Class ID |
|---|---|
| Water | 1 |
| Built-up | 2 |
| Vegetation | 3 |
| Soil | 4 |
The following are the main steps of this tutorial:
- Input Data
- Clustering
- Reclassification of the output
- Refinement of the output
Following the video of this tutorial.