Supervised Classification Using the Semi-Automatic Classification Plugin v. 3.0 "Rome" for QGIS

 Updated tutorial at https://fromgistors.blogspot.com/2016/09/semi-automatic-classification-pluginv5.html

This basic tutorial illustrates how to perform a supervised classification of land cover using the Semi-Automatic Classification Plugin (SCP) 3.0 "Rome" for QGIS.

We are going to classify a subset of Landsat 8 image acquired over Rome, Italy (data available from the U.S. Geological Survey) on June 12, 2014. Using a semi-automatic approach we are going to rapidly classify the image and estimate land cover area, in only six phases.

Minor Update: Semi-Automatic Classification Plugin v. 3.0.1

This post is about a minor update for the Semi-Automatic Classification Plugin for QGIS, version 3.0.1.

Following the changelog:

-fixed a bug with the accuracy assessment

This update fixes an issue with the ID of land cover classes during the accuracy assessment.
The updated version is already available for download.

A Study Using the Semi-Automatic Classification Plugin v. 3.0 for Mapping the Mangrove Forest in Indonesia

After only one day from the release of the Semi-Automatic Classification Plugin v.3 "Rome", I am glad to inform you about the availability of a very nice study for the land cover classification of a mangrove forest using the SCP.

The article is structured in a tutorial way, and guides you from the installation of the SCP, to the land cover classification of a Landsat 8 image acquired over Segara Anakan, Indonesia.

The article is available here, and I would like to thank its author Fatwa Ramdani.

Semi-Automatic Classification Plugin v.3.0 "Rome" released

First version of the plugin

A little over a year ago, I released the first version of the Semi-Automatic Classification Plugin (SCP) for QGIS 1.8, which allowed for the creation of ROIs (Regions of Interest) with a region growing algorithm and the supervised classification of remote sensing images.

The first release had an essential interface that was quite effective for the classification purpose. Also, it relied on several other programs for the ROI collection and the classification process.
In the following months I released several plugin updates, adding new features and changing the interface accordingly.

Today I am glad to announce the release of the new Semi-Automatic Classification Plugin version 3.0 code name "Rome" (the city where the plugin was created) which is the result of a long work and brings several new features and improvements.

The major changes are:

• Reduced dependencies to only: GDAL, OGR, Python Numpy, Python SciPy, and Python Matplotlib;
• Several code improvements and updated interface;
• Classifications are performed using spectral signatures that can be imported from external libraries such as USGS spectral libraries and ASTER spectral libraries;
• Classification previews are faster.

Semi-Automatic Classification Plugin v. 3.0 Rome for QGIS

Available the Ukrainian Translation of the User Manual of the Semi-Automatic Classification Plugin v. 2.5.1

It is with great pleasure that I announce the availability of the Ukrainian Translation of the User Manual of the Semi-Automatic Classification Plugin v. 2.5.1.