Exercise: Brussels¶

For issues, bugs, proposals or remarks, visit the issue tracker.

Objectives¶

Learn to work with the MESMA toolbox in QGIS
Use MESMA as a sub-pixel classification method:

Interpret the results visually

Compare performance when using different libraries

Compare different MESMA techniques

Perform a hard classification on your MESMA result

Tutorial Data Set¶

You can download the tutorial data set here (tutorial_data_set_brussels.zip). The zip file contains the following data:

Apex images from 2015 with numbers 014, 14 and 180 in ENVI format
A spectral library in ENVI format
A validation shape file for each image
Note: images and library have been smoothed using Savitzky-Golay filter with window size 9

Acknowledgement for the data set:

Degerickx, Roberts, Somers; 2019; Enhancing the performance of Multiple Endmember Spectral Mixture Analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection; Volume 221; P 260-273

Note

It is good practice to keep all files in the same folder - especially during the exercises. Files like square arrays often go looking for library information on which they are built.

Image Inspection¶

Try to visualize the images in QGIS.

To recognize the surroundings, overlay them with a map from the OpenStreetMap project (QuickMapServices plugin and Google Satellite View).

Inspect the technical properties of the image.

Why are the images black when first loading them into QGIS? Which bands would you use to visualize them for easy interpretation? Look-up the wavelengths of the RGB bands.

What is the size of the image and of each pixel?

Make a list of the land cover classes you expect to find in each image.

Creating and Optimization of Spectral Libraries¶

See exercises on http://spectral-libraries.readthedocs.io.

MESMA¶

In SMA, a mixed pixel (image) is modelled as a linear combination of endmembers (library spectra) and their fractions.

MESMA wants to account for within-class spectral variability and allows a single land cover class to be represented by multiple endmembers. As a result, the selected EMs vary on a per-pixel basis, allowing dynamic mapping of spectrally complex scenes.

Exercise: MESMA

Post-Processing: Soft to Hard Classification¶

Lastly, we can do a soft to hard classification based on the Fraction Image (under post-processing tools): each pixel will be assigned the dominant fraction (excluding shade).