What is semi-Automatic classification Plugin?

The Semi-Automatic Classification Plugin (SCP) is a free open source plugin for QGIS that allows for the supervised classification of remote sensing images, providing tools for the download, the preprocessing and postprocessing of images. Several algorithms are available for the land cover classification.

How do I add a SCP plugin to QGIS?

How to install SCP from the official repository

1. Run QGIS, from the main menu select Plugins > Manage and Install Plugins;
2. Click Settings then click the button Add;
3. Inside the Repository details enter:

What is supervised classification in QGIS?

Since Remote Sensing software can be very expensive this tutorial will provide an open-source alternative: the Semi-automatic-classification plugin (SCP) in QGIS. Land cover classification allocates every pixel in a raster image to a defined class depending on the spectral signature curve.

How to use semi automatic classification plugin ( SCP )?

The following is a basic tutorial about the land cover classification using the Semi-Automatic Classification Plugin ( SCP ). It is assumed that you have a basic knowledge of QGIS. 5.1.1. Tutorial 1: Your First Land Cover Classification ¶ This is a basic tutorial about the use of SCP for the classification of a multispectral image.

How to enable semi automatic classification in QGIS?

From the menu All, select the Semi-Automatic Classification Plugin – master and click the button Install plugin; the latest version of SCP should be automatically activated (ignore errors, the restart of QGIS could be necessary to complete the SCP installation); it is possible to deactivate the other SCP installed in the QGIS repository; 7.2.

How to add rasters to semi automatic classification plugin?

Open the tab Band set clicking the button in the SCP menu or the SCP dock. Click the button to refresh the layer list, and select the bands: 2, 3, 4, 5, 6, 7, 8, 8A, 11, and 12; then click to add selected rasters to the Band set 1.

How to define the input image for SCP classification?

Now we need to define the Band set which is the input image for SCP classification. Open the tab Band set clicking the button in the SCP menu or the SCP dock. In Band set definition click the button to clear all the bands from active band set created during the previous steps.

What is Semi-Automatic Classification Plugin?

The Semi-Automatic Classification Plugin (SCP) is a free open source plugin for QGIS that allows for the supervised classification of remote sensing images, providing tools for the download, the preprocessing and postprocessing of images. Several algorithms are available for the land cover classification.

How to do image Classification in QGIS?

Supervised classification in QGIS

1. 1 Purpose and Introduction.
2. 2 Installing the Software and SCP.
3. 3 Obtaining the Data. 3.1 Unpack the Data.
4. 4 Load the Data into QGIS and Preprocess it. 4.1 Creating a Band set.
5. 5 Supervised Classification. 5.1 Set Region of Interests (ROI)
6. 6 Assessing the classification.

How to use the semi automatic classification plugin?

Tutorials¶ The following is a basic tutorial about the use of the Semi-Automatic Classification Plugin. Using a semi-automatic approach we are going to rapidly classify a Landsat 8 image and estimate land cover area, in only six phases.

Which is the best plugin for image classification?

One plugin that you will use to perform image classification of satellite imagery is called the Semi-Automatic Plugin. Developed by (Luca 2016), the Semi-Automatic Classification Plugin (SCP) is a free open source plugin for QGIS that allows for the semi-automatic classification (also known as supervised classification) of remote sensing images.

Which is the semi automatic classification plugin for QGIS 3.0?

Using Semi-Automatic Classification Plugin with QGIS 3.0. The Semi-Automatic Classification Plugin version 6.0, updated and designed to work with QGIS 3.0 has a new cool main interface, new tabs and intuitive design.

Are there any errors in semi automatic classification?

Water and vegetation are correctly identified. However, you can see that there are several classification errors (especially soil classified as built-up and vice versa), because the number of ROIs (spectral signatures) is insufficient. We can improve the classification using some of the tools described in the next tutorial.