RGB or a Multispectral Image with LargeScaleMeanShift

Hi all,

I would like to ask you if and how the number of bands of an image could affect the segmentation output
For example, if I have an RGB and a RGB-NIR image, which one would be more useful to have a better segmentation output (ie segments that would fit better the objects on my image)??
I checked on the manual, and the Range radius parameter defines the radius (expressed in radiometry unit) in the multispectral space.

So, would it be more meaningful to use an image with many bands (eg. 40 bands, 10 for each season)? Is it an rule that I need to follow?

I look forward to your response!


Hello Vasilis,

I think you should use the RGB-NIR image. The information contained in the NIR band is probably useful to discriminate between the objects contained in the image.

The use of the 40 bands makes sense if the objects you are trying to segment are not moving during the year. Please note that the mean shift algorithm is based on Euclidian distances, and therefore might be not be adapted to high dimensions. When trying to segment such images, it might be interesting to apply dimensionality reduction as a pre-processing step of the segmentation.

Normalizing your data before the segmentation might also be useful if the input bands have different ranges.

Ultimately, finding the right input and the right parameters for this algorithm requires some trial and error, there is a recent discussion on this subject here

Please note that I am not an expert in this algorithm or in segmentation in general, so take everything I said with a grain of salt :slight_smile: I encourage you to look at the Mean Shift literature to find more information. You can take a look at [1], this paper describes the implementation used in OTB and an application to Pleiades imagery.


[1] Michel, J., Youssefi, D., & Grizonnet, M. (2015). Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 952-964.

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