Minor Update: Semi-Automatic Classification Plugin v. 3.1.2

This post is about a minor update for the Semi-Automatic Classification Plugin for QGIS, version 3.1.2.

Following the changelog:
-fixed bug when exporting band set


Supervised Classifications of Landsat Images: an Overview of Applications Using the Semi-Automatic Classification Plugin for QGIS

Remote sensing is “the science and technology by which the characteristics of objects of interest can be identified, measured or analyzed the characteristics without direct contact” (Japan Association on Remote Sensing, 1993. Remote Sensing Note).
In the past few decades the use of remote sensing has rapidly grown because of the technology advances (e.g. spatial and spectral resolutions of data have largely improved) which fostered the development of new applications related with Geographic Information Systems (GIS) and environmental monitoring.

The land cover classification of remote sensing images is one of these applications.
In the past few months I have posted several tutorials about the land cover classifications of remote sensing images using the Semi-Automatic Classification Plugin for QGIS.
Please, read this article dated 1999 by NASA, which illustrates the main features of land cover classifications using remote sensing images, as well as the importance of spectral signatures in the identification of the land cover types.

Highlighting the importance of the interpretation of remote sensing images, I have described how to perform classifications of multispectral images and hyperspectral images, and specific remote sensing applications for environmental monitoring using a semi-automatic approach.
Following, a brief overview of these applications using Landsat images (available from the U.S. Geological Survey):


Supervised Classification of Hyperspectral Data: a Tutorial Using the Semi-Automatic Classification Plugin for QGIS

This post is about the supervised classification of hyperspectral data using the  Semi-Automatic Classification Plugin for QGIS. In particular, we are going to classify a Hyperion image.
Hyperion is a NASA satellite launched in the frame of the EO-1 project. This hyperspectral satellite has hundreds of bands from the visible to the Short Wavelength Infrared, allowing for the identification of the spectral signatures of materials. Images have 30m resolution pixels (the same as Landsat) and cover a land area of 7.5 km by 100 km.
You can see an example of spectral signatures from Hyperion data in the following image.

Spectral signatures from an Hyperion image (available from the U.S. Geological Survey)

For more information about the Hyperion sensor see here. Hyperion images are freely available from the USGS EarthExplorer website.

In this tutorial we are going to classify the following land cover classes:
  • Class 1 = Vegetation (e.g. grassland or trees);
  • Class 2 = Soil (e.g. soil without vegetation);
  • Class 3 = Built-up (e.g. artificial areas, buildings and asphalt);
  • Class 4 = Water (e.g. surface water).

The following are the main classification steps:
  1. Definition of the input;
  2. Creation of the ROIs and spectral signatures for the image;
  3. Classification of the image.

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I am working in the fields of GIS and Remote Sensing.