What is different about the segmentation implemented in the runShepherdSegmentation binding ?

[Tristan Postadjian]

Hi Pete, hi Daniel,

I would like to know more about the segmentation available in RSGISLib. Indeed, I kept looking for this paper :

Shepherd, J.D.; Bunting, P.; Dymond, J.R. Operational large-scale segmentation of imagery based

on iterative elimination. J. Appl. Remote Sens. 2014, in press.

I found the reference in the paper you wrote :

A Python-Based Open Source System for Geographic

Object-Based Image Analysis (GEOBIA) Utilizing Raster

Attribute Tables

However, no way to find the first one. I cannot see in the description you make of the K-means clustering implemented, a difference with other K-means (sklearn for instance).

I know you mentioned once your own paper because you don't know if the first one is still available, but if you had any more precise idea of what the difference is about, that would be great.

Thanks !

Tristan

[Daniel Clewley]

Hi Tristan,

I’m not sure what is happening with the Shepherd et al., paper. Pete should be able to provide more details later once he returns from leave and things settle down after the start of term.

The k-means clustering is implemented as the ‘RSGISKMeansClusterer’ class within src/math/RSGISClustering.cpp (available to view here), so it is probably best to look at this to get the specifics of the implementation.

Hope this helps,

Dan

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[Angelica Resende]

I keep needing this paper, I believe it would help to better understand segmentation parameters, because my Shepherd segmentation does not segment water.

thanks