Problem dry fields- urban areas on supervised classification in QGIS, Landsat 7

Question

I am trying to make a supervised classification on a Landsat 7 image using the QGIS 3.16.1 version. I am using the SCP plugin to do then classification.  I am doing the classification using the RGB band 4-3-2 and the maximum likelihood classification.The problem I have is that in the final product the bare or dry fields appear like urban areas. Does anyone know how to solve this?

Aaron · Answer

There are several issues you need to address to improve the accuracy of your land cover classification.

Make sure you have a large enough sample of training data and that your training data is distributed equally among all of your classes. A good rule of thumb is to use at least 50 samples for each class for areas <1 million acres and fewer than  12 classes (Congalton, 1988)

Use all of the available bands for your particular sensor. If you are using Landsat 7, certainly incorporate bands 1-5 and 7.

Also include indices such as NDVI, EVI, NDBI, and NDWI. More details here. These will help to further differentiate classes.

Consider incorporating a non-parametric decision tree classifier such as Random Forests.

Reference:
Congalton, R. G. (1988). A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. Photogrammetric engineering and remote sensing (USA).

Problem dry fields- urban areas on supervised classification in QGIS, Landsat 7

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