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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?

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    This is a very common problem. Why not use the additional bands available with Landsat data?
    – Aaron
    Dec 11, 2020 at 21:40
  • So I should select the sample for urban areas with the 4-3-2 band and agricultural areas with 6-5-2 maybe ? Do you have something else to suggest me or is there any other way to fix it?
    – kopa23
    Dec 11, 2020 at 22:33
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    Use all of the available bands for your classification. You may also want to consider incorporating indices such as NDVI, NDBI, and NDWI.
    – Aaron
    Dec 12, 2020 at 1:40

1 Answer 1

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

  1. 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)

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

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

  4. 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).

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  • Thank you very much I will check it
    – kopa23
    Dec 12, 2020 at 12:44

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