I've tried using the classifiers in ArcMap which only supports one ancillary dataset and I tried using Orfeo in QGIS 2.18, though the Orfeo Image Classification tool does not accept any ancillary raster datasets. I can create a training model using multiple bands and images in Orfeo but Image Classification only accepts one raster file so I can't feed it the extra ancillary data I used during the training.

Besides the original satellite image, I want to add in NDVI, NDWI, and DEM's to help the classifier just so you guys have an idea of what I'm trying to do. I haven't come across something like this besides creating a completely custom classifier.

How do I Classify With Multiple Ancillary Datasets using QGIS?

EDIT: I've figured it out. I just make a new composite raster image which includes the ancillary data as additional bands or create a virtual raster catalog. This way I can use the new composite raster with classifiers in any Remote Sensing software which often accepts only 1 raster file at a time. I've already tested this with my low spectral resolution data and it helped my classification output by adding NDVI and a NDWI as extra bands.

  • Maximum Liklihood in ArcGIS allows for the use of multiple bands: desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/…. Also, I would recommend performing the classification using R, which allows you tremendous freedom in setting up your parameters. Here is something to get you started: gis.stackexchange.com/q/39021/8104.
    – Aaron
    Jan 6, 2019 at 15:47
  • I reduced the scope of this question by retrofitting to its first answer. If you also wish to ask how to do it using ArcGIS Desktop then please do that in a separate question.
    – PolyGeo
    Jan 7, 2019 at 1:00
  • I just realized, is it a sensible thing to include supplementary data as additional bands in Remote Sensing Data then do that in MLC? @Aaron For example I have a 4 band image then I just create a new composite image with NDVI and Radar as band 5 and 6 respectively?
    – Esparko
    Jan 7, 2019 at 10:32
  • @pbroto You should be able to include the individual bands including NDVI and radar without creating composite bands.
    – Aaron
    Jan 7, 2019 at 13:40
  • Ah I see now, thanks for the tip. Though from what I can tell this is only available for the MLC classifier in ArcMap? The other methods such as Random Forest/SVM or classifiers in other software might have to use a composite image of bands and ancillary data by the looks of it.
    – Esparko
    Jan 7, 2019 at 15:17

1 Answer 1


Unless I am misunderstanding your needs, I can recommend the use of Semi Automatic Classification plugin in QGIS, which is actively developed and very well documented, and in which a complete dataset composed of any amount of bands of any number of files works as input for the classification.

  • Thanks for suggesting this plugin, it's my first time using it and the classification process is interesting. What I wanted to do is combine RGB+NIR bands for bands 1-4. I'm trying to experiment adding ancillary data to the classification methodology by adding it as a raster band. For example, NDVI values for band 5 and Radar for band 6.
    – Esparko
    Jan 7, 2019 at 15:21
  • @pbroto, the result of a classification is a shapefile with the polygons resulting from the classification. The origin is a dataset of bands of one or more files, it is not necessary that all data be in a single file. Maybe what you're trying to do is create a virtual raster catalog before? Jan 7, 2019 at 15:55

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