[UW-GIS-L] eCognition vs. Feature Analyst

[Stefan E. Coe]

There are some key differences between the two. In simple terms, Feature Analyst is a black box classifier. Its easy to learn, easy to use, and can extract features pretty well using high resolution imagery. It works very much like a traditional supervised classification: you digitize areas of interest that represent the features you wish to extract and these sites are used to parametrize the classifier. The software has some cool features that helps you iteratively train the classifier, e.g. you can select falsely classified features (both errors of omission and commission) to help train the classifier. All in all, it works reasonably well with one caveat: the results are very dependent on the granularity of your classification and the heterogeneity of your scene. I think feature analyst would work quite well if you were trying to map general vegetation classes such forest and grass but maybe not so great if you wanted to break it down into species or seral stage.

As Luke says, eCogntion is very complex, powerful and a bit
overwhelming. The actual classification process is much more up to the user and
there are many different directions one could go (hence the overwhelming
factor). Its primary purpose is to segment the image into objects (polygons)
which form the basis of the features (as opposed to pixels) to be classified. These object can than be used in a traditional supervised classification (called the standard nearest neighbor classifier) or in a rules based classification (e.g. label this object grass if the mean value of band 1 is less than 50) or a combination of the two. You can also do a hierarchical object classification where you have segmentations of different levels of heterogeneity and hence larger super objects and smaller sub objects that are spatially nested. These can then be used to hierarchically break down your image into different classification levels. For example, your top level (larger objects) could be used to separate urban and vegetation objects. Since the sub-objects are nested within the super objects, you can create parent/child rules so you can apply classification rules to your sub-objects based upon their super-object membership.

Wow, I did not intend to write that much. I hope at least part of it makes sense. The bottom line is that if you think you can get away with feature analyst, I would lean in that direction because eCognition takes a considerable time investment to become really proficient. I would be happy to answer any other questions. Good luck,

-Stefan


Stefan Coe
GIS/Remote Sensing Analyst
Urban Ecology Research Lab
University of Washington


Stefan Coe
GIS/Remote Sensing Analyst
Urban Ecology Research Lab
University of Washington
206-616-9379

On Mon, 24 Jul 2006, Luke Rogers wrote:

> eCognition is a big, ugly, complicated, unpredictable and not well
> documented yet very cool and powerful piece of software. I shouldn't but
> I'll put Stefan on the spot since he has used both... I have only used
> eCognition. I put the eCognition user guide here
> (http://www.ruraltech.org/downloads/UserGuide.pdf) which is all the
> documentation you get.
>
> -Luke
>
> -----Original Message-----
> From: uw-gis-l-bounces at mailman1.u.washington.edu
> [mailto:uw-gis-l-bounces at mailman1.u.washington.edu] On Behalf Of
> cwayne at u.washington.edu
> Sent: Monday, July 24, 2006 2:02 PM
> To: uw-gis-l at u.washington.edu
> Subject: [UW-GIS-L] eCognition vs. Feature Analyst
>
> We are considering which software to use for an alliance-level veg mapping
> project, based on color 1-m orthos.  One camp is pushing for eCognition,
> while another favors Feature Analyst.  My reservations for both are based on
> accuracy and the learning curve behind each.
>
> My general impression is that eCognition is more accurate, but harder to
> learn and more expensive.  Can anyone share their experiences, or point to
> me to case studies using either of the above?  Thanks!
>
> caw
>
> Chris Wayne
> GIS Instructor
> University of Washington
> Educational Outreach
> cwayne at u.washington.edu
> http://faculty.washington.edu/cwayne
>
>
>
> _______________________________________________
> Uw-gis-l mailing list
> Uw-gis-l at u.washington.edu
> http://mailman1.u.washington.edu/mailman/listinfo/uw-gis-l
>
> _______________________________________________
> Uw-gis-l mailing list
> Uw-gis-l at u.washington.edu
> http://mailman1.u.washington.edu/mailman/listinfo/uw-gis-l
>