ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
ENV 202/502 INTRODUCTORY REMOTE SENSING
Practical Exercise Week 10 - Object Based Image Analysis
1. OVERVIEW
This practical is designed to provide participants with a very basic introduction to Object
Based Image Analysis (OBIA) by using Ecognition software. This is a very complex software
package, and it is not possible to become competent in its use through a single practical
exercise, though participants should start to be familiar with the different possibilities of using
this type of image analysis.
OBIA is used for generating classifications where the features of interest are larger than an
individual pixel in an image. Similar pixels are grouped together in a process called
‘segmentation’, which creates image objects. The user can then develop a rule set, or
sequence of operations to assign categories to individual objects. The strength of this type of
processing is that you can use the full suite of image interpretation tools that our eyes and
brain would commonly employ without thinking (i.e. texture, shape, size, tone, context,
association), rather than simply using spectral or ‘colour’ information that is used in
traditional pixel-based techniques.
1.1.
Learning Outcomes
After completing this practical, participants will be able to:
1. Navigate their way around the Ecognition user interface to display data, change band
combinations, and use the zooming and panning tools;
2. Load a new project into Ecognition;
3. View the properties of a basic ruleset and understand its operations; and
4. Run a simple rule set to classify a Worldview-2 image into basic landcover
categories.
1.2.
Required data
The below data can be accessed via G:/RESOURCES/ENV202/ecognition
WV2_05062010_subsetdps
Worldview-2 multispectral imagery acquired 5th June 2010, radiometric and atmospheric
corrections applied for use in ecognition.
Darwin Harbour WV2.dpr
Ecognition project containing above image and ruleset for basic classification
1.3.
Required software
Trimble Ecognition 8 (previously Definiens)
Note, if you are an external student, you will need to access the software and data via the
Virtual Server Machine
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
2. GETTING STARTED IN ECOGNITION
Open Ecognition in the Rule Set mode. (when it starts opening, it will give you the option
of ‘Quick Map Mode’ or ‘Rule Set Mode’. You have to be quick to click on ‘Rule Set Mode’ or
it will default to quick map.
Go to File  Open Project (or click the ‘open project button) and navigate to Darwin
Harbour WV2.dpr
Make sure that you are in Develop Rulesets View (or go to View  Develop
Rulesets View)
The Ecognition GUI has a number of different components as per the image below. As you
have loaded a ready-made project, several of these components will already contain
information.
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
Menu / Toolbar
Classes
Rule Set
Image Viewer
Object Info
Algorithms
Click on the Edit image layer mixing button to change the display band combination.
Experiment with different bands by putting a ‘dot’ in the colour gun beside the band that you
would like displayed as that colour (as below)  OK
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
If you want to just view a grey-scale single band image, just use one dot.
You can also use the following other tools to change band combinations. Click on them in
turn to explore what they do.
Test out each of the zooming and panning tools until
you are comfortable with navigating around your image.
3. USING A RULE SET
A rule set allows you to develop decisions on how to classify your image. They require a lot
of experimentation and knowledge of the spatial and spectral relationships within your image
data. Rule sets are created in the ‘Process Tree’ area. Many people using this software
make their rule sets freely available via the ‘Rule Set Exchange’ on www.ecogntion.com –
it’s a good idea to check here first before creating your own rule set to see if anyone has
already solved your problem for you!
The first stage of classification and rule set development in OBIA is called Segmentation.
Here the software groups together neighbouring pixels that have similar spectral properties,
based on some user specified parameters.
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
In the Process Tree, you will see that a rule set has already been loaded. To see how the
image is initially segmented, right click on ‘segmentation’  Execute
This will take about 1.5 minutes to run (note above it took 1:35.550), and is dependent on
the size of the image, and the size and complexity of the objects you wish to create.
Once complete, Click show or hide outlines
You will now see that your image has been ‘chopped up’ into objects. Relatively
homogenous areas (e.g. water) consist of larger objects.
Use your zooming and panning tools to investigate how well the segmentation has
represented the outlines of different features.
Click on some different objects, and their outline will change to red. You will also see
average values for that object in the Image Object Information window.
The next stage is to group together objects into classes.
Right Click Classification – water  Execute
Use your zooming and panning tools to investigate how well the classification has
delineated the water bodies. You may like to experiment with different band combinations to
assess this.
Continue working through the ruleset to classify artificial surfaces, vegetation, and bare
ground.
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
The final stage is to merge all neighbouring objects within the same class into single objects
or polygons ready to export to a GIS.
Right click Tidy up  Execute
In the bottom of the Process Tree window below the ruleset, you can see additional
comments made about each stage of the ruleset. To activate this at any time, hover in the
bottom right of the Process Tree and click the show/hide comment window icon.
To delete the classification at any stage, click the ‘delete level’ button.
Q1. What criteria are used to create each of the classes? (2 points)
Q2. How do you think the classification has performed? What sorts of features have been
classified well, and not so well? Explain (5 points)
Q3. What other rules do you think that you could incorporate to improve classification, and
how (think about the full range of image interpretation cues – tone, colour, texture…)? (5
points)
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ENV 202/502: Introductory Remote Sensing. Practical Exercise Week 10
Q4. Compare this style of classification to what you have previously done in class. Can you
think of any costs or benefits? (4 points)
4. TOP TIPS
1. Understand the importance of context in image analysis;
2. Translating your ‘mind-map’ to a ruleset is a challenging and time consuming
process. Don’t expect to be experts overnight; and
3. Remember that there is no ‘one size fits all’ model and that OBIA will work in some
instances, but not in all.
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