[GRASS-user] object-based classification (vs pixel-based)

["Peter Löwe"]

Hello Moritz,

> Hello,
> 
> Some of my colleagues use eCognition for remote sensing data 
> classification. After having done so for teaching, we are now looking 
> into the possibilities of replacing proprietary with open source 
> solutions in research.
> 
> eCognition uses an object-based approach to classification as opposed to 
> pixel-based. As they put it in their brochure:
> 
> "The technology examines pixels not in isolation, but in context. It
> builds up a picture iteratively, recognizing groups of pixels as objects.
> Just like the human mind, it uses the color, shape, texture and size of
> objects, as well as their context and relationships to draw the same
> conclusions and inferences that an experienced analyst would draw."

The issue about how to emulate the eCog-approach into GRASS has been around for a while. Here are my two eurocents of knowledge:

eCog consists of several algorithms. First, a segmenting tool (patented...) is used to divide a multispectral image stack into segments. Then, a knowledge-based (fuzzy) merging process gets you to the object-representation. 

So to do it with GRASS, we need to have a multispectral segmenting tool plus "the object stuff". 

I am not sure if there are any FOSS-segmenting tools in the works that do their job without signature files [medical imaging tools, maybe?].

For the next step, the abstraction from spatial segments to objects, object-oriented grass-friendly environments such as R or CLIPS/CAPE could be used to create a prototype/demonstrator. However, the performance for large data-sets might be significantly below a c/c++ implementation. 

Peter

> Is something like this possible in GRASS ?
> Or are there any other open source solutions using this approach ?
> 
> Moritz

-- 
Dr. Peter Löwe
<peter.loewe at gmx.de>





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