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?
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.
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).