Monday, 14 December 2015

eCognition Tutorial: How to find segments which have lower mean value to the neighbouring segments with additional condition to class?

I have segmented data, classified into two classes: 1, 2. I would like to find segments into the 1 class which are adjacent to the 2 class and have lower mean value. The one condition should be: Existence of 2 > 0, but how to combine it with information about lower mean value of segment?

This is a problem posted in the eCognition community by one of the user. One of the core strength of OBIA is to incorporate contextual information and class related information in the process which is difficult with pixel-based approaches. Here the class of interest has to satisfy two contextual class related information:

1)      It must be bordering the class 2

2)      It must be class 1 and must have lower mean value
The Problem

For this problem, we have to make a class related feature ( Class-Related features >  Relation to neigbor objects > Mean diff. to  ) that is based on a layer of interest and class. For demonstration purpose I will be using NIR layer. So the feature that is created is “Mean diff to nir, class 2”. In the following figure, we can see the feature “Mean diff to nir, class 2” on the right side. In the figure, objects that are not bordering the class 2 have undefined value (red), the objects that are bordering the class 2 and have lower “Mean diff to nir, class 2”,  have smaller value (darker) and the objects that are bordering the class 2 and have higher “Mean diff to nir, class 2”,  have higher value (brighter).

The custom feature
For better illustration I have attached some figures that also show values for “Mean diff to nir, class 2” feature.

Class 2 object

Class 1 object not bordering class 2

Class 2 objects bordering Class 1

Unclassified object bordering Class 1
Afterwards, the extraction of objects of interest is straight forward. We use assign class algorithm for that purpose.
Assign class

The solution. Pink color represent objects we are attempting to extract.


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