I have been
very busy lately, so apologies. But on the flip side I am writing something
that may be useful for many of you. eCognition is pioneer software for doing
object based image classification. Let’s say, you have two images classified,
time T1 and time T2 and you want to identify change and no change areas.
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| Time T1 |
 |
| Time T2 |
 |
| Change/ No Change map to be produced |
You would proceed
like this:
1) Copy Map T1 to Map Change detection
in lvl T1
2) Copy lvl T1 above and name as lvl T2
3) Synchronize Maps T2 with lvl T2 in
Map Change Detection.
4) At lvl T2Ł convert to sub object
5) Copy lvl T2 in Map Change Detection.above and
name as lvl change
6) Delete classification in lvl change
By doing
these steps, you have a level, lvl change where each object is of same size as
in the sub objects in lvl T1 and lvl T2.
Now you want to classify objects in lvl change as change and no change. How
would you proceed?
Majority of
people proceed like this:
1) Set up a class No change, and look
for change in class for each objects
using ‘Existence of sub object”’ in sub objects in lvl T1 and lvl T2
(c.f. figure). Figure shows set up of rule
set for one class only.
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| Class description when one class only |
2) Generally classification has many numbers of
classes. When adding
one more class in the rule, the class description of No change class becomes a
bit complex.
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| Class description when two classes only |
3) Now imagine you have 20 different classes.
Of course you can perform the process by adapting the rule description for No
change for another 18 class. But it’s a tedious process and takes time.
Further, the same class description cannot be used for another set of
classification maps which may have different class names.
4) Wise, things to do is to take benefit
of array handlings capabilities of eCognition, concept of sub-object,
super-object and Parent Process Object (PPO).I am showing you a solution that
is short and does not need any set up of class description in change and No
change class, easily adaptable to other projects.
6) I leave up to you to try to
understand the rule set shown above. If you don’t understand, give me a shout, I will try to explain.
I guess this is the only tutorial I found on net about change detection using eCognition. I am truly appreciated for the meaningful sharing. But I have one basic question. first step is to input both classified image, T1 and T2, isnt it? But how to separate the classified image to different level? Do we need to perform segmentation at both images T1 and T2?
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