I have been trying to run the k means cluster analysis on a sentinel-1 image, even after trying to change the no of clusters and iterations, snap is unable to process the data. Need a solution for this.

Do you get an error message?

Howm many input features are you using?

after clicking run,

this dialog box pops up. it does not finish the process. To clarify, my processor has a speed of 2.8ghz and 8gb ram. Reducing the no of clusters or iterations has not helped as well.

clustering takes time - but it strongly depends on the number of input features. Is it simply VV and VH or do you use additional rasters?

it is simply a vv band, My concern was whether a SAR image needs to undergo histogram equalization or other kind of a standardization in order for the values to be processed by the k means algorithm.

well, clustering actually depends on a multi-dimensional feature space to be effective because it computes the mean distance to cluster centers. With only one input your distances are computed along a line and you simply divide your data into several value ranges of VV. Besides that, Intensity or Sigma0 is mostly gamma distributed (95% of values are below 0.5, but the remaining ones can range up to 500), which is not ideal for clustering. Have you considered converting them to dB scale so the histogram has a more gaussian distribution?

Thank you for your insight on the k means algorithm, and yes i have converted the band to db before inputting it into the k means algorithmâ€¦

Hey @josh09dmello did you finally manage to get your k means clustering right? My image has also totally failed to be clustered, Process just cant stop writing. I have already converted to db @ABraun

Please include some more details from your side: what data you work with,size of the raster, how many bands you use for clustering, how many clustersâ€¦

I am working with setinel 1 GRD image. The image is about 7mbs. But the whole file is about 4.6gb. I am using sigmavh and sigma vv as my bands. And 7 clusters, 30 iterations

Can you please create a small subset and rum the clustering again to test if the size is the problem?

I am clustering a masked area.Would it make a difference if i did create a subset before masking?

maybe the mask is part of the problem. To narrow it down, please compare

- clustering a small area without mask
- clustering a small area with mask

If small areas work, maybe the size is too big. If only the one without mask works, maybe the mask has introduced the problem.