Update: Semi-Automatic OS v. 2.0.2


This post is to inform you about the availability of the updated virtual machine: Semi-Automatic OS v. 2.0.2.

This new version of the Semi-Automatic OS (a lightweight virtual machine for the land cover classification of remote sensing images and GIS analyses) includes the Semi-Automatic Classification Plugin 2.3.1 for QGIS 2.0.1, already configured along with all the required dependencies (Orfeo Toolbox, SAGA GIS, GDAL, Numpy and Matplotlib).

Semi-Automatic OS 2

Flash Update: Semi-Automatic Classification Plugin v. 2.3.1


Flash update of the Semi-Automatic Classification Plugin for QGIS 2 to version 2.3.1.

The changelog:

-fixed a bug with the calculation of ROI signature


This is a minor update, which fixes a bug with the calculation of ROI signatures in the Spectral signature tab.

Update: Semi-Automatic Classification Plugin v. 2.3.0


I have released a major update for the Semi-Automatic Classification Plugin for QGIS 2.0, version 2.3.0.

This is the changelog:
-new Pre processing tab with utilities
-Pre processing tab: clip multiple rasters at once with coordinates or shapefile
-Pre processing tab: Landsat (4, 5, 7, and 8) utility for automatic conversion
to TOA Reflectance and At-Satellite Brightness Temperature, and optionally DOS1
atmosperic correction
-Post processing tab: classification report in pixel, percentage and area
-added language checkbox for switching between English and locale (default
English)
-Land cover change calculation now uses SAGA command changedetection
-bug fixing

This version features a new Pre processing tab with utilities for preparing raster bands to classification.
In particular, I have added the ability to clip multiple raster at once, using coordinates or a shapefile.
Clip multiple rasters tab

Also, a new tab allows for the automatic conversion of Landsat bands (Landsat 4, 5, 7, and 8) to TOA Reflectance and At-Satellite Brightness Temperature (for thermal bands). Also, it allows for a simple atmospheric correction using the DOS1 method (Dark Object Subtraction 1), which is an image-based technique.
It requires the metadata file (MTL.txt) which is provided with Landsat images. This process automatically convert every .tif file that is found in a defined directory, if the file name ends with a band number. For more information about the conversion equations, see here.
Landsat tab

A new Post processing tab allows for the creation of a report of land cover classification,  providing the pixel count, the percentage and area of each class. This is also available as option during the classification process.
Classification report tab

Finally, the land cover change calculation now uses SAGA command changedetection, which is rapider than the previous python code.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.

Minor Update: Semi-Automatic Classification Plugin v. 2.2.3


New update of the Semi-Automatic Classification Plugin for QGIS 2 to version 2.2.3.


The changelog:

-fixed a bug with the selection of a qml style

This is a minor update that fixes a bug with the selection of a .qml file, which is used as the default style for classifications and previews.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.
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