Exercise: Run IES¶
Data used¶
We will use the Spectral Library (library.sli) from the testdata. To analyse this library first, use the spectral library tool . In this tool, use the Open Library button and browse to this file.
How many spectra does this library contain?
What are the different classes and subclasses in this library and how many are there?
To help you interpret the different classes, have a look at table 2 in the paper of Jeroen Degerickx (find it in your exercise folder):
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
Exercise¶
Spectral Library:
- Select library.sli.
- The reflectance scale factor is automatically detected as 1. This is correct.
- The metadata element we are using for this analysis is Meta2.
Set constraints:
- We leave the constraints settings as they are.
Forced IES:
- We will not use this option in this exercise.
Output:
- The default output name is library_ies.sli.
Click RUN to run IES. The progress should be shown on the log-tab. But since this is a very heavy process, QGIS might freeze for the duration of the process. IES is quite slow and might take a while.
Result¶
A summary of the algorithm’s progress is saved as library_ies_summary.txt and automatically opens up on the screen. This summary file lists for each iteration the endmember name, kappa value and confusion matrix:
IES SUMMARY
----------------------------------------
Based on the spectral library: C:\Users\...\library.sli
Metadata
Cass header: Meta2
Unique classes: EXGR LVEG PAVEMENT ROOF SHRUB SOIL TREE
Used a forced library? No
...
ITERATIVE PROCESS
----------------------------------------
Loop 0: new endmember: tiler598 (597)
- Kappa at this point: 0.08008494
- Confusion Matrix:
EXGR LVEG PAVEMENT ROOF SHRUB SOIL TREE Unclas
EXGR 0 0 0 0 0 0 0 0
LVEG 0 0 0 0 0 0 0 0
PAVEMENT 0 0 0 0 0 0 0 0
ROOF 0 0 36 91 0 2 0 0
SHRUB 0 0 0 0 0 0 0 0
SOIL 0 0 0 0 0 0 0 0
TREE 0 0 0 0 0 0 0 0
Unclas 14 80 122 124 60 45 59 0
Loop 1: new endmember: dbt199 (198)
- Kappa at this point: 0.160418
- Confusion Matrix:
EXGR LVEG PAVEMENT ROOF SHRUB SOIL TREE Unclas
EXGR 0 0 0 0 0 0 0 0
LVEG 0 0 0 0 0 0 0 0
PAVEMENT 0 0 0 0 0 0 0 0
ROOF 0 0 36 91 0 2 0 0
SHRUB 0 0 0 0 0 0 0 0
SOIL 0 0 0 0 0 0 0 0
TREE 0 11 0 0 32 0 55 0
Unclas 14 69 122 124 28 45 4 0
From this file, we can see that the first endmember selected was tiler598. This is a roof spectrum that modeled 91 out of 215 roof spectra but also 36 pavement spectra and 2 soil spectra generating a kappa of 0.08. Thus, using this one spectrum our accuracy would be about 8% and a majority of the library would be unclassified.
Examine the second iteration. You should see dbt199. This is a tree spectrum that correctly classifies 55 out of 59 tree spectra, but also a majority of the shrub vegetation and some of the low vegetation. The kappa has increased to 0.16.
The final library consists of 92 spectra and the kappa value has reached 0.85. |
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