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Using eCognition Developer in Quick Map Mode

TABLE OF CONTENTS

1. Using eCognition Developer in Quickmap Mode

1. Purpose

2. Workflow

3. GUI framework

1. Building a solution

2. Defining action parameters

3. Loading and saving analysis settings as solutions

4. Running a solution

4. Use Segmentation Actions

1. Segmentation (Quadtree)

2. Segmentation (Multiresolution)

3. Segmentation (Merge Objects)

5. Use Classification Actions

1. Classification (Optimal Box Classifier)

2. Classification (Clutterremoval)

3. Classification (Nearest Neighbor)

4. Classification (Brightness Threshold)

6. Export Data

1. Export (Points)

2. Export (Polygons)

Using eCognition Developer in Quickmap Mode

PurposeThe Quickmap Mode is designed to allow a user solving simple analysis tasks without having

to get involved with ruleset development. main steps in analyzing an image are creating

objects, classifying objects and exporting results. For each of these steps, a small assortment

of actions is available. These actions can be combined freely utilizing the Analysis Builder

framework offered by the Definiens Architect and Developer products. Using the Quickmap

Mode, the user can either build a new analysis, starting with data only, or he can build on

existing Definiens projects.

Actions ▼

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Figure: Quickmap Mode GUI

WorkflowThe Quickmap Mode supports two basic workflows. The user can either start with new data

or build on existing Definiens projects.

Workflow 1: Start with new data

When starting with new data, the first thing which needs to be done is creating a new project.

Once data is loaded, the first action needs to be a segmentation action which actually

generates an object level. This can be either the quadtree or the multiresolution

segmentation. Once an image object level is generated, a classification action can be added.

This can be the nearest neighbor, the optimal box or the brightness threshold action. From

there you can merge areas, continue classifying, resegment areas or simply export results.

Workflow 2: Build on existing project

Working with an existing project allows segmenting and classifying just as with new data only

with the difference, that you can start classifying right away if image objects are available.

When working with existing projects some points need to be considered:

Make sure that first, the action library is opened and then the project is loaded.

Opening an action library after the project will delete all existing levels and results.

Only classes which are flagged “display always” are displayed in the action dropdown

menus. If classes exist which are not displayed, you need to change this accordingly or

ask the ruleset developer to do so.

GUI frameworkThe Quickmap Mode is operated within the Application Builder framework. If you start

Definiens Developer in the Quickmap Mode, the application is automatically opened.



To open the library in eCognition developer when running in Ruleset Mode, go to Library >

Open Action Library and open the action library stored in the bin\application folder of you

eCognition 8 installation:

eCognition 8.0\bin\applications\QuickMapMode\ActionLibrary.

Building a solutionWhen building a solution the following operations are necessary:

Add action

Press the (+) symbol to add an action.

Remove action

Press the (-) symboil to remove an action.

Change action sequence

Use the ^ symbol to change the action sequence.

Defining action parametersAction parameters can be defined in the parameter field of an action. Various GUI

components are available, which can be set by inserting values, pressing buttons or moving

sliders. The individual parameters are explained in the section which details the individual

actions.

Loading and saving analysis settings as solutionsYou can save the analysis settings in the Analysis Builder as a solution file (extension .dax)

and load them again.

Figure: Architect Toolbar

Save Solution

1. To save the analysis settings, do one of the following:

Click the Save Solution to a File button on the Architect toolbar.

Choose Library > Save Solution on the main menu bar.

Alternatively, you can encrypt the solution by using one of the following:

Click the Save Solution Read-only button on the Architect toolbar.

Choose Library > Save Solution Read-only on the main menu bar.

2. Enter a file name and a directory location and click OK.

Load Solution

Load an already existing solution with all analysis settings from a solution file

(extension .dax) to the Analysis Builder window.

1. To load a solution, do one of the following:

Click the Load a Solution From File button on the Architect toolbar.



Choose Library > Load Solution on the main menu bar.

To use a Solution that was built with another Action Library, open the Action Library

before opening your solution.

2. Select the desired file and click OK. The solution is displayed in the Analysis

Running a solutionThere are different ways to run a solution or components of a solution. You can either utilize

buttons implemented in the individual actions or use the Architect toolbar functions. All

functions implemented with the individual actions will be explained with the respective

actions. Below is an overview of the functions offered by the Architect toolbar.

Figure: Architect Toolbar

Run selected action

Click the Run selected action button to run a selected action. This button is used to execute

an action after the definition is set.

Run solution until selected action

Click the Run solution until selected action button to delete the existing status and rerun the

entire solution until the selected action. If you build on existing projects you should not use

this button, since all existing objects and results are removed.

Execute solution

Click the Execute solution button to delete the existing status and run the entire solution. If

you build on existing projects you should not use this button, since all existing objects and

results are removed.

Use Segmentation Actions

Use segmentation actions to create image objects or to merge objects in the same class.

Three segmentation actions are provided:

Quadtree:

This action divides images into squares of different sizes, depending on the homogeneity of

the image. Homogeneous areas will have larger image objects than complex or

heterogeneous areas.

Multiresolution:

The multiresolution segmentation algorithm consecutively merges pixels or existing image

objects. It is based on a pairwise region merging technique.

Merge Objects:

This algorithm merges image objects that are in the same level and class into larger image

objects.

Note

Whenever a segmentation action is added to a solution, existing samples of the classes

which are segmented are disconnected. This means that the samples feature space is



stored, but the physical sample object is removed. For the user this means the sample

cannot be removed any more.

Segmentation (Quadtree)Use the Segmentation (Quadtree) action to create image objects. It uses the quadtree based

segmentation algorithm. The quad tree based segmentation algorithm creates squares of

differing sizes by cutting. Thus it is a top-down segmentation algorithm. You can define an

upper limit of color differences within each square. This limit is called Scale parameter.

The following are examples of typical uses:

Images with a well-separated background/foreground situation, for example, some

cellular assays.

Images with a significant proportion of background that is easy to separate.

To increase the processing performance significantly for this type of image, apply a quad tree

based segmentation before separating the background. Then use a different segmentation

for the non-background areas of interest.

To use the Segmentation (Quadtree) action:

1. Click the plus

sign (+) or the

Add new Generic

Action link in the

Analysis Builder

window. Select the

action, and then

click Addand

Close.

2. In the Domain

group box, select

a level or

selectcreate new

level to open

the Enter

Name of the new

Level dialog box

and enter a name

or accept the

default. Click OK .

3. In

theDomain group

box, select a class

or selectcreate

new class to

open the Create

Figure: Segmentation (Quadtree) Action



New Class dialog

box and enter a

name or accept

the default.

Click OK .

4. Select theUse

Thematic

Layers if you

want to include

thematic data.

5. Use the slider

in the Scalearea

to select the scale

of objects. A

higher scale will

tend to create

larger objects.

6. Move your

mouse over fields

in the Analysis

Builder window to

see descriptions

in the Description

area.

7. Run the action

by clicking Run

8. Alternatively

you can run the

action by clicking

onAnalysis >

Run Selected

Action in the

main menu bar.

After running the

action, you can see the

resulting image objects

by clicking in the

project view or by

clicking the Show or

Hide Outlinesbutton

to see all image

objects outlined.

Segmentation (Multiresolution)Use the Segmentation (Multiresolution) action to create image objects. It uses the

multiresolution segmentation algorithm. The multiresolution segmentation algorithm

consecutively merges pixels or existing image objects. Thus it is a bottom-up segmentation



algorithm based on a pairwise region merging technique.

Multiresolution segmentation is an optimization procedure which, for a given number of image

objects, minimizes the average heterogeneity and maximizes their respective homogeneity.

The following are examples of typical uses:

Extracting features which are characterized not purely by their color but also by

certain shape homogeneity.

Extracting land cover or man made features from remote sensing imagery.

To use the Segmentation

(Multiresolution) action:

1. Click the plus

sign(+) or the Add

new Generic Action link

in the Analysis Builder

window. Select

Segmentation

(Multiresolution) in

the Add Action dialog

box; then click Add

and Close.

2. In

the Domaingroup

box, select an Input

Level, and an Output

Level using the drop-

down arrows for the

Level fields.

3. Choose from the

available values or

select create new level

to open the Enter

Name of the new

Level dialog box and

enter a name or accept

the default. Click OK . If

the output level equals

the input level, the

input level will be re-

segmented.

4. Select a Class in

theDomain group

box. Select from the

available values or

select create new class

to open the Create

Figure: Segmentation (Multiresolution) Action.



New Class dialog

box, name a new

class, select a color

and click OK.

5. Select the Use

Thematic

Layerscheckbox if you

want to include

thematic data.

6. Use the Scale slider

in the Settings area to

select the scale of

objects. A higher scale

will tend to create

larger objects.

7. Use the Color slider

in the Settings area to

determine the weight

of color as a

parameters in the

segmentation result.

Higher values will tend

to produce results with

greater emphasis on

the color of image

objects.

8. Move your mouse

over fields in the

Analysis Builder

window to see

descriptions in the

Description area.

9. Run the action by

clicking Run

10. Alternatively you

can run the action by

clicking on Analysis >

Run Selected

Actionin the main

menu bar.

After running the action, you can see the resulting image objects by clicking in the project

view or by clicking the Show or Hide Outlines button to see all image objects outlined.

Segmentation (Merge Objects)Use the Segmentation (Merge Objects) action to merge objects that are in the same class into

larger objects.



You must have classified image objects to use this Action.

To use the

Segmentation (Merge

Objects) action:

1. Click the plus

sign (+)or the Add

new Generic Action

link in the Analysis

Builder window.

SelectSegmentation

(Merge Objects) in

the Add Action

dialog box; then

click Addand Close .

2. In

theDomain group

box, select a Level

using the drop-down

arrow. Choose from

the available values.

ClickOK . This is the

level where objects

will be merged.

3. Select a Class in

the Domain group

box. Select from the

available values.

Only objects in the

selected class will be

merged.

4. Select theUse

Thematic

Layerscheckbox if

you want to include

thematic data.


clicking Run

6. Alternatively you

can run the action by

clicking onAnalysis

> Run Selected

Action in the main

menu bar.

After running the Action,

you can see the resulting

image objects by clicking

in the project view or by

Figure: Segmentation (Merge Objects)



clicking the Show or Hide

Outlines button to see all

image objects outlined.

Use Classification ActionsUse classification actions to classify image objects based on samples or on thresholds. Four

classification algorithms are available:

Optimal Box

The Optimal Box classifier is a sample based classification algorithm. Based on user defined

samples, the best feature set for separating the two input classes is derived and the

classification is performed.

Nearest Neighbor

The Nearest Neighbor classifier is a sample based classification algorithm. Based on user

defined samples, a nearest neighbor algorithm combined with predefined feature sets is

used to assign objects to classes.

Brightness Threshold

This action classifies objects based on a brightness threshold. Objects darker than a defined

threshold are labeled with the defined class.

Clutter removal

The Clutter Removal action assigns and merges objects below a defined size threshold to the

predominant class in the surrounding. This action is not a pure classification action. It

performs both a classification and a merge.

Note

You must first run a Segmentation Action to create image objects befor being able to

run classification actions

Classification (Optimal Box Classifier)Use the Classification Box (Optimal Box Classifier) action to classify image objects based on

samples.

To use the Classification Box (Optimal

Box Classifier) action:

1. Click the plus sign (+) in the Analysis

Builder window, select Classification

(Optimal Box Classifier) in the Add

Action dialog box and click Add and

Close to add it.

2. In the Domain group box, select a

Level where image objects that you want

to classify exist.

3. Select an Input Class from the

drop-down list in the Domain group

box. These are the image objects that

will be classified.



4. Select a Positive Output Class and

a Negative Output Class from the

drop-down list in the Domain group

box, or create new class names by

selectingcreate new name . These will

be the two new classes into which the

Input Class will be divided.

5. Select a Feature Space from the

drop-down list. The Feature Space is

the type of feature used to classify

image objects.Color is using pixel

values only, Color & Shape refers to

pixel values and shape combined. Color

& Texture refers to pixel values and

texture combined. The color feature

space classifies image objects based on

the image pixel values. The color &

texture feature space adds haralick

texture measures for improved results.

The Color & Shape adds shape features

such as roundness etc. to allow

separating objects based on their

shape.

6. Select positive samples, by clicking

the Positive Sample Selection magic

wand button and then double-clicking

on image objects in the project view to

select them as samples of the

prospective class.

7. Each sample will have the color that

you selected when you created

thePositive Output Class.

8. Next click the Negative Sample

Selection magic wand button and

choose some negative samples in

project view by clicking on image

objects. Each sample will have the color

that you selected when you created the

Negative Output Class. If you need to

deselect a sample, click on it again. To

change the assignment of an individual

sample, change the sample input mode

and select the sample again.

9. Click Apply to apply the new classes

to the entire project.

10. Select more samples as needed and

click Apply again.

11. Add samples from a vector or raster

file, if needed.

Figure: Classification (Optimal Box

Classifier) Action.



12. Click Reset All Samples button to

delete all samples and start over.

13. Once the results are satisfying, save

the solution .

Samples can be trained in an iterative

mode using several input images. To

do so, do the following:

1. Train the action on one image set,

save the solution and open a second

image.

2. Run all steps of the solution until the

classification step you want to train.

3. Add samples

4. Once the results are satisfying, save

the solution

Classification (Clutterremoval)Use the Classification (Clutterremoval) action to remove image objects below a defined size

threshold.

To use the

Classification

(Clutterremoval)

action:

1. Click the plus

sign (+) in the

Analysis Builder

window,

selectClassification

(Clutterremoval) in

the Add Action

dialog box and click

Add and Close to

add it.

2. In the Domain

group box, select a

Level where you

want to remove

clutter.

3. Select a Class for

which you want to

remove clutter.

4. Enter a Maximum

Area for the size of

the image objects.

The unit depends on

Figure: Classification (Clutter Removal) Action.



the setting used

when the scene was

imported.


going toAnalysis >

Run Selected

Actionin the main

menu bar. The

objects in the

selected class will be

removed.

6. To preserve the

classification, save

the solution or apply

a new segmentation.

Classification (Nearest Neighbor)Use the Classification (Nearest Neighbor) action to classify image objects by using samples.

To use the Classification (Nearest

Neighbor) action:

1. Click the plus sign (+) in the Analysis

Builder window, select Classification

(Nearest Neighbor) in the Add Action

dialog box and click Add and Close to

add it.

2. In the Domain group box, select a

Level that includes the image objects you

want to classify.

3. Select a Input Class to which the

classifications will be applied.

4. Select a Positive Output Class and

aNegative Output Class from the

drop-down list in the Domain group box,

or create new class names by selecting

create new name. The members of the

input class will be assigned or reassigned

to the classes in the output fields.

5. Select a feature space (Color, Color

& Texture, or Color & Mutual

Embedding) from the Feature Space

drop-down list. The color feature space

classifies image objects based on the

image pixel values. The color & texture

feature space adds haralick texture

measures for improved results. The Color

& Mutual Embedding adds local context

Figure: Classification (Nearest Neighbor

Classifier) Action.



to the color feature space. It allows

separating areas by context, which

cannot be distinguished on color alone.

6. Select positive samples, by clicking

thePositive Sample Selection magic

wand button and then double-clicking on

image objects in the project view to

select them as samples of the

prospective class. Each sample will have

the color that you selected when you

created the Positive Output Class.

7. Next click the Negative Sample

Selection magic wand button and

choose some negative samples in project

view by clicking on image objects.

8. Click the Preview Classification-

Run button to preview the resulting

classifications.

9. Click the Reset button to delete all

samples and start over.

10. To finalize the classification, save the

solution or add another segmentation

action.

Classification (Brightness Threshold)Use the Classification (Brightness Threshold) action to classify objects based on brightness.

To use the

Classification

(Brightness Threshold)

action:

1. Click the plus

sign (+) in the

Analysis Builder

window and

selectClassification

(Brightness

Threshold) in the

Add Action dialog

box. Click Addand

Close.

2. In the Domain

group box, select a

Level using the

drop-down arrow.

Choose from the

available values or

select create new



level to open the

Enter Name of the

new Level dialog box

and enter a name or

accept the default.

ClickOK .

3. Select an Input

Class using the

drop-down arrow.

4. Select an Output

Class using the

drop-down arrow.

Choose from the

available values or

select create new to

open the Create

New Class dialog

box and enter a

name and color or

accept the default.

Click OK .

5. In the Settings

area, move the

Threshold slider to

select a brightness

threshold. Objects

with brightness up to

the selected

threshold will be

classified.

6. In the Preview

group box, click

the Test

Classification-

Run button to

preview the

classification. The

classified objects

display in the

project view.

7. Click the Reset

Classification-

Run button to delete

the classifications if

needed.

8. To preserve the

classification, save

the solution or apply

a new segmentation.

Figure: Classification (Brightness Threshold) Action



Export DataThe Quickmap Modeprovides three data export actions:

• Export Points

• Export Polygons

Export (Points)Use the Export (Points) action to export object coordinates as a set of vertices.

To export

points:

1. Click the plus

sign (+) in the

Analysis Builder

window and

selectExport

(Points) in the

Add Action dialog

box. ClickAdd and

Close.

2. Run the action

by going

toAnalysis > Run

Selected

Actionin the main

menu bar.

Figure: Export (Points) Action.

Export (Polygons)Use the Export (Polygons) action to export an ESRI .shp file containing the object outlines. The

desktop export location is the image file folder.

To export

points:

1. Click the plus

sign (+) in the

Analysis Builder

window and

selectExport

(Polygons) in the

Add Action dialog

box. Click Add and

Close.

2. Run the action

by going

toAnalysis> Run

Selected Actionin

the main menu bar.

Figure: Export (Polygons) Action.

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