object orientated course manuel

Unit 1: Introduction Level:

• Beginner Time:

• This unit should not take you more than 1 hour Resources:

• A licence of Definiens Developer (Version 7.0 was used to develop these units).

• An image you can load into Definiens for this exercise. For this unit the Landsat 7 image orthol7_20423xs100999.img (Row: 204 Path: 23 Date 10/09/1999) over North Wales and available from the Landmap service is recommended.

By the end of this unit you should:

• Be aware of the purpose of the Definiens Developer software and the types of projects the software can be used for.

• Know how to create a new project within Definiens and the options available for creating a project.

• Know the main elements of the Definiens Developer user interface. • Know how to change the viewing properties.

1.1. Background This section will provide you with a brief outline of the purpose of the Definiens software and an overview of the collection of software tools Definiens have to offer. For a more detail on Definiens products please visit the Definiens website http://www.definiens.com or contact them directly. 1.1.1 Purpose The purpose of the Definiens Developer is to facilitate the development of object oriented rule based classification procedures. Therefore, rather than simply independently classifying the individual pixels within the scene the image is split (segmented) into regions representing objects within the scene. Working with objects rather than pixels have numerous benefits over traditional pixel based analysis, for example, the spatial relationship between objects can be represented or the shape of an object analysed. Definiens Developer provides an easy to use (although relatively step learning curve) interface to represent the classification rules and visual scripting interface (processes) to control the segmentation and classification process. 1.1.2 The Suite of Definiens Tools Definiens Developer is one of a number of tools which Definiens produce which together form the Definiens Enterprise Image IntelligenceTM suite (Figure 1).

http://www.definiens.com/

Figure 1.1. Definiens Enterprise Image Intelligence™ Suite, Client and Server software.

(Source: Definiens.com) The software can be divided into three categories End-user, Developer and Server-side, where the use of each depends on your role within the image processing chain. The end-user products include Definiens Architect, Definiens Analyst and Definiens Viewer. Definiens Viewer is the simplest of the three and intended for a user to simply view results that have been previously processed. Definiens Analyst allows a use to import and execute fully automatic classification processes (previously developed) and view the results. Finally, Definiens Architect provides a framework within which a user can run fully and semi automatic image analysis programs, written using Definiens processes, and provides a mechanism for manual correction of results. Definiens Developer encapsulates the functionality of the Viewer, Analyst and Architect products but with the additional functionality to develop the ruleware (classification algorithms) required to process image data. Finally, the Definiens eCognitionTM Server provides functionality to process your images through a dataset type infrastructure where the ruleware (developed using Definiens Developer) can be executed simultaneously across a series of servers. Additionally, this infrastructure supports automatic tiling and stitching of images and results to allow very large images to be efficiently processed. 1.1.3. Help while using Definiens Definiens provide customer (and user to user) support through their online forums (http://forum.definiens.com/index.php) where you can post your problems or read previous answers. Additionally there is a section where you can download sample rulesets. Definiens also make a number of documents available online including presentations, case studies, white papers and scientific papers (http://www.definiens.com/resource-center_61_24_0.html).

http://forum.definiens.com/index.php

http://www.definiens.com/resource-center_61_24_0.html

Additionally, your installation of Definiens Developer contains sample data and a user guide alongside a technical reference guide which contains a wealth of information/ 1.2. Technical Specification For a full technical specification of the software please refer to the Definiens User Guide and Reference Guide. These documents are usual install alongside the software C:\Program Files\Definiens Developer 7.0\UserGuides. 1.3. Understanding the User Interface 1.3.1. Starting Definiens Developer Definiens Developer is normally available from the Windows start menu: Start > All Programs > Definiens Developer 7.0 1.3.2 The User Interface Once started, you will be presented with an interface similar to the one shown in Figure 1.2.

Figure 1.2. The User interface for Definiens Developer 7.0.

Where the interface is different, from Figure 1.2, you will need to toggle the view buttons . The view buttons result in the following interfaces, Figure 1.3, but for this series of units you will only require the Developer interface (4) as shown in Figure 1.2.

1) The workspace interface 2) Analysis Interface

3) Results Interface 4) Developer Interface

Figure 1.3. The interface views available with Definiens Developer. 1.3.2.1 Components of the User Interface

Figure 1.4. The user interface with the components labelled.

Data Viewer: The image and classification data viewer. The viewer allows you to view the imagery you are classifying, including manipulating the band order and image stretching. Process Tree: The window within which you develop your ruleset script. Class Hierarchy: The window displaying the classes you develop. Image Object Information: This window displays selected feature values for a selected object. Feature View: This window displays a list of all the available features within Definiens Developer and allows the current image objects to be coloured (green high values and blue low values) given their value for a select feature. 1.3.2.2 Toolbar Icons Table 1.1 provides a glossary of the icon available on the various toolbars within Definiens Developer.

Icon Description File Toolbar

Create New Project

Open Existing Project

Save Project

New Workspace

Open Workspace

Save Workspace

Predefined Import View Settings Toolbar

Workspace view

Analysis View

Results View

Developer View

View Image data

View Classification

View Samples (for Nearest Neighbour Classification)

Feature View

Toggle Object Means and Pixel Data

Toggle Object Outlines

Toggle Polygons

Toggle Skeletons

Toggle Image View and Project Pixel View.

Single Layer (Grey Scale)

Mix Three Layers RGB

Show Previous Layer

Show Next Layer

Select Layers to be Displayed and Image Stretch View Navigation Toolbar

Delete Level

Select Level For Display

Down a Level

Up a Level Tools Toolbar

Object Information

Object Table

Undo process

Redo process

Class hierarchy

Process tree

Feature View

Managed customised features

Toggle manual editing toolbar Zoom Toolbar

Manual Editing Toolbar

Single Selection

Polygon Selection

Line Selection

Rectangle Selection

Cut Object

Merge Object Selection

Merge Selected Objects

Clear Merge Object Selection

Filter Classes for Multiple Image Object Selection

Classify Image Objects Samples Toolbar

Select Samples

Drag and Click Brush to Assign Image Object Samples

Sample Editor

Sample Selection Information

Toggle Sample Navigation Table 1.1. A glossary of toolbar icons.

1.4. Creating Your First Definiens Developer Project The first step when using Definiens Developer is to load your data into the software. During these units this will always be done through the creation of an individual project. If you later require to process very large datasets or a large number of individual images a workspace provides a convenient and efficient way to store and manage these data. Please refer to Definiens Developer user guide for information about workspaces. To create your new project either select File > New Project or select the new

project icon ( ). You will be presented with the following dialog box (Figure 1.5a) through which you will enter your datasets and parameters to create your project. In this example you will load the image orthol7_20423xs100999.img (Figure 1.5b), by selecting ‘Insert’ next to the Image Layer List. Once you have loaded your data you need to define the

layer aliases, as shown in Figure 1.5b, where bands 1-6 correspond with aliases BLUE, GREEN, RED, NIR SWIR1 and SWIR2, respectively. To bring up the layer properties dialog double click on each image band in turn, or select the band and select on the edit button.

a) Empty Create Project Dialog b) Create Project Dialog with Layer

Properties Figure 1.5. Shows the create project dialog.

You can also add thematic information in the list below the image layers list which could be used during your classification and segmentation, for example a polygon shapefile of building. The next step is to give your project a name, in this case call it ‘Example 1’ and check the projection information for your image has been correctly read. If this information is incorrect then you need to check the ‘Pixel size (unit)’ on the right-hand side. The ‘Pixel size (unit)’ should be set to ‘auto’ and the unit to ‘meters’ and the ‘Use geocoding’ option ticked on. You also have the option of re-sampling you imagery to a resolution of your choice using the ‘Resolution (m/pxl)’ dialog box and to select a subset using the ‘Subset Selection’ button, which present a dialog similar to the one shown in Figure 1.6. To select a subset you can either draw a red box on the image in the dialog or provide the pixel limits of your subset. Before finalising the project and selecting OK we will subset the image (as shown in Figure 1.6), where minimum X is set to 4600, the maximum X is set to 5200, the minimum Y is set to 4400 and maximum Y is set to 5000.

Figure 1.6. Create a subset of the datasets for the project.

Now click OK to create the project and you will move back to the Definiens Developer interface, Figure 1.7.

Figure 1.7. The Definiens Developer interface once the project has been loaded.

1.5. Using the Display Options within Definiens Developer 1.5.1. Using the Zoom Toolbar. Once the project has been loaded you can pan and zoom around the data, in the display region, using the zoom toolbar, shown below in Figure 1.8.

Figure 1.8. Zoom Functions toolbar

If the zoom functions toolbar is not displayed you can turn it on using the View>Toolbars menu. 1.5.2. Selecting bands for Display To select the layer(s) to be displayed you need to use the ‘Edit Image Layer

Mixing’ dialog, Figure 1.9, available via the following icon .

Figure 1.9. The Edit Image Layer Mixing dialog.

Using the ‘Layer Mixing’ drop down menu you can select the number of layers to be mixed in the display and then by selecting the individual layers you may turn then on and off (or increase the weight), Figure 1.20.

a) One layer grey level b) 3 layer RBG model c) Six layer mixing

Figure 1.20. Selecting the layers for display. Also, you can adjust the equalisation (or stretch) of the data layers being displayed using the ‘Equalizing’ drop down menu. The available options are ‘Linear (1.00%)’, ‘Standard Deviation (3.00)’ ‘Gamma Correction (0.50)’, ‘Histogram’ and ‘Manual’. 1.5.3. Multiple Views Definiens Developer also allows you to split your display, therefore allowing you to have multiple views of the same data. This functionality is available from the Window menu (Figure 5.21.).

Figure 5.21. The Window Menu.

Here the current display can be split horizontally and/or vertically and once split can be ‘linked’ to provide views which automatically move together. Once you have split your screen by selecting the window you wish to change the same tools as outlined above can be used to manipulate the display properties in each of the different views. 1.6. Conclusion In summary, you should now be able to open Definiens Developer, create a project and manipulate the display to view the data as you wish. The following unitswill take you through the segmentation and classification of the imagery you have loaded into your project and some more advanced features of the Definiens software. 1.7. Exercises

1) Experiment with the layer properties, such that you can view each image band individually and then a number of 3 and 6 band mixings. Observe how the different land cover types visually change as you change the band mixings. 2) Using the layer combination of your choice (R: NIR G: SWIR1 B: RED, is recommended) experiment wit the image equalisations available. Again, observe how the various land cover types change to these changes. 3) Produce a four way split of the display (i.e., a vertical and horizontal split) and set each region to different viewing properties. Finally, link all four together (side by side).

Unit 2: Image Segmentation Level:

• Beginner Time:

• This unit should not take you more than 1.5 hours Resources:


• An image you can load into Definiens for this exercise. For this unit the multispectral Landsat 7 image orthol7_20423xs100999.img and its corresponding panchromatic scene o20423_pan.tif (Row: 204 Path: 23 Date 10/09/1999) over North Wales and available from the Landmap Service is recommended.

Processes:

• RulesetTemplate.dcp • Chessboard_Segmentation.dcp • Quadtree_Segmentation.dcp • Multiresolution_Segmentation.dcp • SpectralDifference_Segmentation.dcp • ContrastSplit_Segmentation.dcp • ContrastFilter_Segmentation.dcp


• Be able to apply each of the segmentation techniques available with Definiens Developer to an image.

• Be aware of the difference between the various segmentation algorithms and the types of objects (size and shape) they each produce.

2.1. Introduction Segmentation is always the first step of any process within Definiens Developer as it generates the image objects on which the classification process will be performed. The important part is for the segmentation process to identify objects which a representative of the features you wish to classify and are distinct in terms of the features available within Definiens (e.g., spectral values, shape, texture). 2.2. Setup a Project As with all work within Definiens Developer the first step is to create a project containing all the datasets required for the study. Although, it is important to keep an eye on the size of the images you are creating a project with, as Definiens Developer can become very slow with very large datasets, due to

the number of objects generated during the segmentation process. Therefore, a subset of the inputted images will be created once again. Your project should have the same parameters as that shown in Figures 2.1.a and 2.1.b.

a) The project parameters b) The subset parameters Figure 2.1. The parameters for setting up the Definiens Project.

Please note the order in which the image files have been loaded, i.e., the panchromatic band first, as this will decide on the image resolution for the project. In this case the 25 m multispectral landsat 7 data will be resampled to the 15 m of the panchromatic data. Once you have matched your project window to those shown in Figure 2.1. select OK and create your project. 2.3. Setup your data display For these exercises it is recommend that you split the display horizontally (Window menu), where one of the windows contains the panchromatic data (Figure 2.2a) and the other the multispectral data using the band combination NIR, SWIR1 and RED as the red, green and blue components (Figure 2.2b), respectfully (Figure 2.3).

a) The layer mixing properties for the

panchromatic display b) The layer mixing properties for the

multispectral display. Figure 2.2. The layer mixing properties for the split windows.

Figure 2.3. The Definiens Developer interface with the project and display parameters defined 2.4. Setup your Process Tree

The process tree ( ) will contain the script that you produce to control the processes (algorithms) which run and the order in which they are executed. It is important to keep the script that you produce during your segmentation and classification procedures as organised as possible, this will allow you to understand what you have done when you come back to it. With this in mind Figure 2.4 contains the template to which you should aim to adhere to.

Figure 2.4. Template Process Tree.

To insert a process right-click within the process tree window and the following menu will appear, Figure 2.5. Select ‘Append New’ and the Edit Process dialog will appear, Figure 2.6.

Figure 2.5. Process tree context menu.

Figure 2.6. Edit process dialog

The ‘Edit Process’ dialog is made up of 6 elements. Each of which will become clear as you move through the notes. Name: The name of the process. This can either be manual entered or automatically provided by the software. A good convention is to only manually edit the name where nothing else within the process has been changed, otherwise use the automatic. Finally, the note icon ( ) allows a comment to be written about the process. Algorithm: The algorithm to execute. This drop down menu allows you to select the algorithm you wish to execute, there is an extensive list of algorithms a number of which will be used during these units. Image Object Domain: The image object domain defines the object(s) on which the algorithm will be executed. The drop down box and ‘Parameter’ box allow the level to be selected. The following button (‘all objects’) allows a class(es) to be defined while the final button (‘no condition’) allows a rule to be used, for example, area > 20 m2. Loops & Cycles: It is possible to allow a process to form a loop, often in the form of a while loop and the tick box allows this to be selected. Algorithm Description: A simple description of the algorithm you are using. Algorithm Parameters: These are the parameters which are associated with algorithm which has been selected. To recreated the template shown in Figure 2.4, edit the name of the process to be ‘Process Template’ and select the comments button and enter ‘This is a template ruleset which most process trees will adhere to.’, the rest of the process should be left unchanged. Select OK, you have now created your first process which will simply execute any process which is create beneath it. To create the next process ‘Segmentation’ right-click’ of the process you have just created and select ‘Insert Child’, this will create a new process under your previous process. Edit the name of this new process to be ‘Segmentation’ and select OK. Select the ‘Segmentation’ you have just created right-click and select ‘Append New’, edit this name of this process to be ‘Classification’. Now repeat this to add the processes ‘Merge’ and ‘Export’. To move processes you can drag and drop them while holding down the left mouse button. To place a process under another process drag and drop holding down the right mouse button.

Finally, you can save and load your process independently of your project (although, your process is saved within the project), this is done by right-clicking within the process tree window and selecting ‘Save Rule Set…’. Alongside the contains of your process tree this will also save any classes or customised features you have created, which are associated with you process. 2.5. Multi-Resolution Segmentation The first and most general segmentation technique available within Definiens Developer is the Multi-Resolution segmentation. To insert this algorithm within your process tree right-click on your ‘Segmentation’ process in the template you previous entered and select ‘Insert Child’. Within the following dialog box select the algorithm as ‘multiresolution segmentation’. If this algorithm is not available scroll to the bottom of the list and select more and move the algorithms you wish to have in the list to the right-hand column. You should now be presented with the dialog shown in Figure 2.7. Table 2.1 describes the parameters available for this segmentation algorithm.

Figure 2.7. The Multi-Resolution segmentation process.

Table 2.1. An overview of parameters for segmentation

Parameter Description Level Name Name of the level in the hierarchy created by the

segmentation.

Image Layer Weights

Increases the weighting of the layer when calculating the heterogeneity measure used to decide whether pixels/objects are merged. Zero ignores the layer.

Thematic Layer usage

If any thematic layers are available, allows thematic layers to be turned on and off individually for use within the segmentation.

Scale Parameter Controls the amount of spectral variation within objects and therefore their resultant size. Has no unit.

Shape - Colour A weighting between the objects shape and its spectral colour whereby if 0, only the colour is considered whereas if > 0, the objects shape along with the colour are considered and therefore less fractal boundaries are produced. The higher the value, the more that shape is considered.

Compactness A weighting for representing the compactness of the objects formed during the segmentation.

The multiresolution segmentation creates objects using an iterative algorithm, whereby objects (starting with individual pixels) are grouped until a threshold representing the upper object variance is reached. The variance threshold (scale parameter) is weighted with shape parameters (with separation of shape and compactness parameters) to minimize the fractal borders of the objects. By increasing the variance threshold larger objects will be created although their exact size and dimensions is dependant on the underlying data. To run the segmentation process, leave the parameters at their default values and click execute but it is recommended that you give your level a suitable name. A common level name convention is to number them, starting with Level 1. Once you are happy with the parameters and have executed the process you should have successful completed your first segmentation. Once the segmentation has been executed, select the ‘Show or Hide Outlines’

icon ( ) and the outlines of the objects (segments) created will be displayed over the image. Making sure you have the cursor in the ‘cursor mode’ rather than the ’zoom mode’, select the objects (with either the outlines turned on or

off) in turn. Using the ‘Image Object Information’ window ( ), you will see the values for features associated with selected object (e.g., band values) displayed. To select the features displayed in the ‘Image Object Information’ window, right click within the ‘Image Object Information’ window and select Select Features to Display. By double clicking on the feature, you can move them from one side of the displayed menu to the other. The right hand side contains those features which will be displayed when selecting an object. 2. 2.5.1 Simple Exercise The aim of this exercise is to get used to the multi-resolution segmentation and experiment with the parameters outlined above. The process is the same as the one you implemented above but the segmentation parameters can be altered almost indefinitely, but your task is to alter the parameters in such a way as to achieve the best segmentation over the scene. At this point in time, you may wish to ignore part of the image (e.g., the field areas) and

concentrate on the upland areas or forests and then switch to concentrate on another area. You should find that each of these areas require slightly different segmentation parameters, which we will deal with in later units. Spend approximately 30 minutes experimenting with different segmentation input parameters and observe the differences in the images objects created. Initially try the following:

• High Scale factor • Low Scale factor • High Shape weighting • Low Shape weighting • High Compactness weighting • Low Compactness weighting • Use just the panchromatic layer (set all other layer to weight zero).

To remove your segmentation and try new parameters, you need to delete the level before re-executing your segmentation process, this is done using the

“Delete Level” icon ( ). An example is shown in Figure 2.8. However, there is no definitive answer as to whether one segmentation is better than another and the final selection depends upon whether you are satisfied that the objects you are interested in classifying are adequately delineated.

Figure 2.8. Segmentation of the Landsat imagery.

2.6. Quad-tree Segmentation

A quad-tree segmentation creates regular square objects, where the size is defined by the objects variation. Unlike the multi-resolution segmentation, the objects are created by dividing larger objects until the resultant objects are all within the upper boundary of allowed variation. As with the multi-resolution segmentation, the variation at which a final object is created is defined using a scale parameter. 2.6.1. Simple Exercise To create a quad-tree segmentation, follow the same procedure used to create the multi-resolution segmentation but select the quad-tree segmentation algorithm instead. A scale factor of 20 is recommended for the segmentation, although others might be more appropriate. Try several scale factors for representing a) the fields, b) the uplands, c) forests and d) compromise for all surfaces. Write these down below. Scale Factor Fields: ___________ Scale Factor Uplands: ____________ Scale Factor Forests: ______________ Scale Factor Compromise for all surfaces _____________ 2.7. Chessboard Segmentation A chessboard segmentation is the simplest segmentation available as it just splits the image into square objects with a size predefined by the user. The segmentation does not consider the underlying data and therefore when large objects are created, the features within the data you are trying to classify will not be delineated. This segmentation tends to be used in more advanced processes where segmentation is undertaken in a number of steps combined with a classification. An example is the tree crown delineation algorithm created within Definiens Developer for delineation of crowns within mixed forests in Australia (Bunting and Lucas 2006). 2.7.1. Simple Exercise To perform a chessboard segmentation, setup the process in the same way as the multi-resolution and quad-tree segmentations, but select the chessboard segmentation. To being with, use a value of 1 for the segmentation (This will generate objects of 1 x 1 pixel) and then progressively increase this value. Notice, in each case, how the boundaries and spectral information of the underlying data are ignored. 2.8. Contrast Split Segmentation The aim of this algorithm is to split bright and dark objects using a threshold that maximises the contrast been the resulting bright objects (consisting of

pixel values above the thresholds) and dark objects (consisting of pixel values below the threshold). The algorithm aims to optimize this separation by considering different pixel values, within the range provided by the user parameters, with values selected based on the inputted step size and stepping parameter. Table 2.2 provides a list of the parameters for the algorithm. Parameter Description Chessboard Tile Size

If no level is already present then a chessboard segmentation is undertaken to generate a set of large objects which are iterated through during the segmentation process.

Minimum Threshold

The minimum grey-level value that will be considered for splitting

Maximum Threshold

The maximum grey-level value that will be considered for splitting.

Step Size The sizes of the steps the algorithm will use to move from the minimum threshold to the maximum threshold. Large values will make the algorithm quicker to calculate but smaller values will tend to produce better results.

Stepping Type Either: Add – Calculate each step by adding the value in the scan step field. Multiply – Calculate each step by multiplying by the value in the scan step field.

Image Layer The image layer where on which the algorithm will be applied.

Class for Bright Objects

The class the brighter objects (above the threshold) will be given.

Class for Dark Objects

The class the darker objects (below the threshold) will be given.

Contrast Mode The method the algorithm uses to calculate contrast between bright and dark objects. The algorithm uses the mean of possible bright border pixels and the mean of possible dark border pixels to calculate either the edge ratio or edge difference which can be used to define the contrast between two objects. Alternatively, the object mean (of all pixels) within the bright and dark pixels can be used.

Execute Splitting

If yes objects will be split while if no only the threshold value will be calculated.

Best Threshold A variable (see units 8) to store the threshold that

maximises the contrast.

Best Contrast A variable to store the best contrast calculated.

Minimum Rel. Area Dark

The minimum (relative) area identified as dark objects for the segmentation to be performed. Range 0-1.

Minimum Rel. Area Bright

The minimum (relative) area identified as bright objects for the segmentation to be performed. Range 0-1.

Minimum Contrast

The minimum contrast threshold for the segmentation to occur.

Minimum Object Size

The minimum object size for the segmentation to take place.

Table 2.2. The parameters associated with the contrast split segmentation. To execute this algorithm you will need to create two classes, one for the bright objects and one for the dark objects. To do this within Definiens Developer right-click within the class hierarchy window and select New Class, you do not need to enter any parameters at this point, so just select OK. 2.8.1. Simple Exercise As with the other segmentation algorithms try to achieve the best segmentation of the landscape you can using this algorithm. Remember to investigate all the parameters to observe there effect of the final segmentation. 2.9. Spectral Difference Segmentation This is a merging algorithm where neighbouring objects with a spectral mean below the threshold given (maximum spectral difference) will be merged to produce the final objects. To use this segmentation algorithm you are required to already have a segmentation (level) in place you cannot create a new level using this algorithm. 2.9.1. Simple Exercise Using one of the segmentations you have previously generated extend your process to include a spectral difference segmentation process. Remember, to add a process right-click and select ‘Append new process…”, in this case on the previous segmentation process. Again try to achieve a segmentation that you think is the best for the landscape within the scene. 2.10. Contrast Filter Segmentation

The contrast filter segmentation uses a combination of two pixel filters to create a thematic raster layer, with the values no object, object in first layer (filter response 1), object in second layer (filter response 2), object in both layers and ignored by threshold. Finally, the raster segmentation is converted to Definiens image objects using a chessboard segmentation. The parameters for the algorithm are outlined in Tables 2.3, 2.4 and 2.5. Parameter Description Chessboard Settings

The chessboard segmentation parameters for producing the final segmentation from the filter results.

Layer The image layer on which the filters will be applied.

Scale 1-4 The scale parameters can be used to define membership to the various classes. See the reference manual for more information.

Gradient A minimum gradient threshold

Lower Threshold

Pixels with a filter response below this threshold will be assigned to the ‘ignored by threshold’ class.

Upper Threshold

Pixels with a filter response above this threshold will be assigned to the ‘ignored by threshold’ class.

Table 2.3. The main parameters for the contrast filter segmentation. The contrast filter segmentation allows the definition of shape parameters to control the outputted image objects, the param Parameter Description Shape Criteria Value

Larger values reduce the inclusion of irregularly shaped objects.

Working on Class.

The class (see class assignment) on which the operation will be applied.

Table 2.4. The shape parameters for the contrast filter segmentation. Parameter Description Enable Class Assignment

If set as no, the remaining parameters are not used.

No Objects The class to be pixels with the value ‘no objects’ will be given.

Ignored by Threshold

The class to be pixels with the value ‘Ignored by Threshold’ will be given.

Object in First Layer

The class to be pixels with the value ‘Object in First Layer’ will be given.

Object in Second Layer

The class to be pixels with the value ‘Object in Second Layer’ will be given.

Object in Both layers

The class to be pixels with the value ‘Object in Both layers’ will be given.

Table 2.5. The classification parameters for the contrast filter segmentation. 2.10.1. Simple Exercise As with the previous simple exercises experiment with this algorithm to achieve a segmentation on the image provided. Although, as you will see this algorithm does not produce results on par with the other algorithms outlined in this unit for the image subset provided. 2.11. Conclusion Following the completion of this unit you should have knowledge of all the segmentation processes available within Definiens Developer and implemented each of the algorithms on the image provided. 2.12. Exercises 1) Decide on the most appropriate segmentation algorithm for segmenting this scene. As you are doing this think of what elements you think provide a good segmentation and how the different characteristics of the various algorithms could be used to achieve the segmentation you require.

Unit 3: Nearest Neighbour Classification Level:

• Beginner Time:



• The multispectral Landsat 7 image orthol7_20423xs100999.img and its corresponding panchromatic scene o20423_pan.tif (Row: 204 Path: 23 Date 10/09/1999) over North Wales and available from the Landmap service.

Processes:

• NN_Classification_Process.dcp By the end of this unit you should:

• Be able to complete all the steps require in the process tree to complete a classification (using the nearest neighbour classifier) within Definiens Developer.

• Be aware of the parameters and features to aid the nearest neighbour classification.

• Be aware of the classification, merge and export processes. 3.1. Introduction Within in this worksheet you will create a nearest neighbour classification of a segmented Landsat 7 image of the area around Llyn Brenig, Denbigh Moors, North Wales (Figure 3.1). This area contains extensive tracts of upland heath and bog as well as coniferous forest plantations and grasslands at various levels of improvement.

Figure 3.1. Ordnance Survey Map of the study area

(http://www.ordnancesurvey.co.uk/getamap). The Nearest Neighbour (NN) classifier is a supervised classification approach whereby for each of the classes required, training samples are located and used to classify all remaining (unknown) objects in the image. The NN classifier has been used successfully for many classification problems (e.g., tree species; Leckie et al., 2005). 3.2. Create a project As with all work within Definiens Developer the first step is to create a project containing all the datasets required for the study. A subset of the inputted images will be created once again. Your project should have the same parameters as that shown in Figures 3.2.a and 3.2.b.


Please note the order in which the image files have been loaded, i.e., the panchromatic band first, as this will decide on the image resolution for the project. In this case the 25 m multispectral Landsat 7 data will be resampled to the 15 m of the panchromatic data. Once you have matched your project window to those shown in Figure 3.2. select OK and create your project. 3.3. Setup your Class Hierarchy For classification, the first task is to create the classes you require and (in this case) to insert the Nearest Neighbour Feature into each class. To create a class, you require the ‘Class Hierarchy’ window (shown in Figure 3.3) to be

open. If the window is not already visible, then click on the icon.

Figure 3.3: Class Hierarchy Window before inserting any classes.

To insert a class, right click in the Class Hierarchy window and select ‘Insert Class’ (Figure 3.4).

Figure 3.4. Inserting a class into the class hierarchy.

This provides you with an empty ‘Class Description’ (Figure 3.5).

Figure 3.5. Empty Class Description.

The next step is to edit your class description by first giving your class a name. For example, give the class the name “Water” and assign a blue

colour. When you have done this, insert and name new classes of “Forest”, “Other vegetation” and “Not Vegetation”. You should then have four classes inserted and named:

• Water • Forest • Other Vegetation • Not Vegetation

After giving each class a name, select an appropriate colour for each. This can be anything you wish, although the final classification will be easier to understand and interpret if you chose a logical colour (e.g., Green for Forest). Next, the features (e.g., mean object spectral response) to be used for classification (in this case, the standard nearest neighbour algorithm) need to be inserted into the class. To do this, right-click on the ‘and (min)’ and select ‘Insert new Expression’ (Figure 3.6).

Figure 3.6. Inserting a new expression into the class.

This will present the window (Figure 3.7), where you need to select ‘Standard Nearest Neighbour’ and click Insert.

Figure 3.7. Selecting the expression to be used for the classification.

Your resulting class description should be similar to that shown in Figure 3.8 for the forest class.

Figure 3.8. The resulting class description to be used for the classification.

The same procedure now needs to be repeated for the remaining three classes so that you end up with a classification hierarchy similar to that shown in Figure 3.9.

Figure 3.9 The final class hierarchy. To select the features used for the nearest neighbour classification use the ‘Edit Standard NN feature Space…’ function, Figure 3.10a, where initially you should just use the mean spectral values of the objects, Figure 3.10b.

a) The menu for editing the NN feature space

b) The dialog for selecting the features within the NN feature space

Figure 3.10. Editing the features used within the nearest neighbour classification. 3.4. Setting up the Process Tree 3.4.1 Segmentation As with all classifications in Definiens Developer, the first task is to perform a segmentation. In this case the use of a multi-resolution segmentation is recommended, although others could be investigated. As with previous units, it is recommend you create an outline within your process tree mirroring that outlined in Figure 3.11. Remember, a process is created by right-clicking in the process tree window and select ‘Append Process’ or ‘Insert Child Process’.

Figure 3.11: Process outline and Segmentation process.

To create the process that performs the segmentation, right-click on the Segmentation process you have already created, select ‘Insert Child Process’, and then the algorithm ‘Multiresolution segmentation’. Choose the parameters shown in Figure 3.12 and once you have entered these parameters, click on ‘Execute’ to perform the segmentation.

Figure 3.12. Parameters used for the segmentation of the Landsat image.

Note, that the layer weighting for the panchromatic band (PAN) has been increased to 2. This is in take advantage of the extra spatial resolution of the panchromatic band, 15 m rather than 25 m of the multispectral. 3.4.2. Classification To run the classification, you need to add a classification process to your process tree. This is achieved by right-clicking on the process you named ‘Classification’ and selecting ‘Insert Child Process’. Edit the new process such that it is similar in appearance to that shown in Figure 3.13. To select multiple classes, use the ‘Shift’ and ‘Control’ keys as you would in Windows Explorer.

Figure 3.13. The process parameters to be used for the classification.

After inputting the parameters into the process, click on the ‘OK button at the bottom, you need to select samples before performing your classification. Your process tree should now be similar to that shown in Figure 3.14.

Figure 3.14: The process tree after the inclusion of the classification process.

3.4.3 Merge Result The next step is to setup the processes which will merge your classification so that all neighbouring objects of the same class will form single objects. It is important to merge your classification to identify complete objects. For example, once merged you can query the lake to find its complete area. To merge the result you will need to enter a merge process for each class (Figure 3.16; ‘Insert Child’), the merge parameters for the Forest class are shown in Figure 3.15. The class for merging is defined using the Image Object Domain where the class of interest is defined, if you were to select multiple classes all the select classes would be merged, removing the boundaries and classification of these objects.

Figure 3.15. The process parameters to merge the Forest class.

To save time, once you have created you first merge process you can copy-and-paste (ctrl-c, ctrl-v or right-click on the process) this process to duplicate it and then edit the class you wish to merge.

Figure 3.16. The process tree including the merge processes.

3.4.4. Export Result Finally, we usually wish to export the classification result from Definiens into a GIS for further processing or the production of a map. Therefore, our final process will be to export the classification to an ESRI shapefile, Figure 3.17.

Figure 3.17. Process parameters to export the classification as a shapefile.

To select the classes to export you again edit the Image Object Domain, remember these parameters define the image objects the process will be applied to. The name of the outputted shapefile has been defined as ‘Classification’ while the features to be exported are the area (of the image object) and the class name. Area is found under Object Features > Shape > Generic while class name is found under Class-Related features > Relations to Classification > Class name. For the class name feature you will need to create it, right-click on the ‘Create new Class name’ and select Create, leave the parameters as their default values and just select OK. The shapefile will output to the directory within which your project is saved, if you have not yet saved your project then the shapefile will be outputted to the directory containing the input imagery. You final process tree should then be the same as the one shown below in Figure 3.18.

Figure 3.18. Final process tree.

3.5. Select Samples to Train Classifier

The next stage is to select the samples for each of the four classes, you need to have executed the segmentation process before undertaking these steps If you are unsure of the distribution of ground cover types, please refer to the shapefile LlynBrenig_BasicLandcover.shp. To create a sample, you need to first activate the tool for sample selection (Select Samples) as shown in Figure 3.19.

Figure 3.19 Activating sample selection.

Once you have activated sample selection, highlight the class you wish create a sample for in the class hierarchy window. Either double click on the objects you wish to select as samples or hold down the Shift key and use a single click. To unselect a sample, repeat the process of selection for each chosen object. To aid the selection of your samples, Definiens Developer offers two windows (both available from the menu in Figure 3.19) of information based on the selected samples. Firstly the ‘Sample Editor’ window (Figure 3.20) and secondly the ‘Sample Selection Information’ window (Figure 3.21).

Figure 3.20. Sample Editor Window

The Sample Editor provides a visual comparison of two classes using a range of selected features. In Figure 3.20 the Forest and Water classes are compared using the object means from each spectral band of the Landsat data. When an object is selected, a red arrow is displayed to illustrate where the object mean fits in relation to the mean of the other samples. To change the displayed features, right-click within the main window and select ‘Features to Display…’ or, if you only want the features being used within the NN calculation, select ‘Display Standard Nearest Neighbour Features’.

Figure 3.21: Sample Selection Information Window

The Sample Selection Information window displays information on the NN membership boundaries and the distances to the other classes of the selected object from the selected samples. To select the classes to be displayed, right-click within the window and select ‘Select classes to Display’. Classes displayed in red have, for the selected object, an overlap in the distance measure and therefore the samples may need to be re-analysed and altered. To set the threshold at which a class is highlighted in red, right-click in the

Sample Selection Information window and select ‘Modify critical sample membership overlap’. The threshold ranges from 0 - 1, and 0 highlights any overlap. Once you have selected your samples, you should have an image which is similar in appearance to that shown in Figure 3.22. Bear in mind that the selection of samples does not have a correct answer. Just select the samples you consider to be most representative of the classes you wish to separate and which also give the best separation in the Sample Editor and Sample Selection windows.

Figure 3.22: Samples selected for the nearest neighbour classification.

3.6. Run your Process. Now execute the classification process you previously created (right-click and select execute on the classification process). You should now have a nicely classified image similar to that in Figure 3.18. If you are unhappy with the classification, repeat the procedure but select more or alternative samples before reclassifying the image. To re-run the classification, open the process and simply click on execute or select the process and press F5 or right-click on the process and select ‘execute’.

Figure 3.18. Final classification of the Landsat data.

3.6.1. Merge the Image Objects Once you are happy with your classification, execute the merge image objects processes you have previous created, your results should appear similar to those shown in Figure 3.19b.

a) Before merging b) After Merging

Figure 3.19. Before and after merging classes.

The purpose of merging is to create a final classification which is a closer representation to the objects in the scene. For example, we will now be able to calculate the area of the whole lake. But, be aware that merging the image objects removes your samples, as the segmentation will have changed. 3.6.2. Export the Results Finally, run the process to export the results. This will result in an ESRI shapefile and allow the creation of a map such as the one shown in Figure 3.20.

Figure 3.20. A map produced using ESRI ArcMap from the result of the Definiens

classification 3.7. Feature Space Optimization Tool To refine the classification further Definiens Developer offers an automated feature, the feature space optimization tool, to automatically identify the features which ‘best’ separate the classes for which samples have been selected. To use this feature delete your classification (delete level) and re-run the segmentation process. You will also need to re-select your samples as these will be deleted each time you change the segmentation (i.e., merge or delete level).

Once you have selected your samples open the feature space optimization tool, Figures 3.21 and 3.22.

Figure 3.21. The NN classification menu.

Figure 3.22. The Feature space optimization tool.

To use this tool, select the features you wish to compare – Initially try the mean, standard deviation and the pixel ratio but later try other combinations. Then select calculate, once the calculation has finished then select advanced to see which features offered the best separation, and ‘Apply to the Std NN’ to use within the classification. You can now run your classification step.

3.8. Conclusions Following the completion of this unit you should now understand basis the process of classification within Definiens Developer where future examples will simply build more complex classification and segmentation routines. 3.9. Exercises 1) Experiment with different segmentation parameters, both within the multi-resolution segmentation and the other segmentation algorithms. Be aware that you will have to select new samples each time you delete the level. 2) Experiment with different sets of features within the standard NN feature space. (Classification > Nearest Neighbor > Edit Standard NN feature space…) 3) Experiment with different sets of features and maximum dimension levels within the feature optimisation tool. (Classification > Nearest Neighbor > Feature Space Optimisation) References Leckie, D.G., Gougeon, F.A., Tinis, S., Nelson, T., Burnett, C.N., & Paradine, D. (2005). Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sensing of Environment, 94, 311-326.

Unit 3: Nearest Neighbour Classification Level:

• Beginner Time:



• The multispectral Landsat 7 image orthol7_20423xs100999.img and its corresponding panchromatic scene o20423_pan.tif (Row: 204 Path: 23 Date 10/09/1999) over North Wales and available from the Landmap service.

Processes:

• NN_Classification_Process.dcp By the end of this unit you should:

• Be able to complete all the steps require in the process tree to complete a classification (using the nearest neighbour classifier) within Definiens Developer.

• Be aware of the parameters and features to aid the nearest neighbour classification.

• Be aware of the classification, merge and export processes. 3.1. Introduction Within in this worksheet you will create a nearest neighbour classification of a segmented Landsat 7 image of the area around Llyn Brenig, Denbigh Moors, North Wales (Figure 3.1). This area contains extensive tracts of upland heath and bog as well as coniferous forest plantations and grasslands at various levels of improvement.

Figure 3.1. Ordnance Survey Map of the study area

(http://www.ordnancesurvey.co.uk/getamap). The Nearest Neighbour (NN) classifier is a supervised classification approach whereby for each of the classes required, training samples are located and used to classify all remaining (unknown) objects in the image. The NN classifier has been used successfully for many classification problems (e.g., tree species; Leckie et al., 2005). 3.2. Create a project As with all work within Definiens Developer the first step is to create a project containing all the datasets required for the study. A subset of the inputted images will be created once again. Your project should have the same parameters as that shown in Figures 3.2.a and 3.2.b.


Please note the order in which the image files have been loaded, i.e., the panchromatic band first, as this will decide on the image resolution for the project. In this case the 25 m multispectral Landsat 7 data will be resampled to the 15 m of the panchromatic data. Once you have matched your project window to those shown in Figure 3.2. select OK and create your project. 3.3. Setup your Class Hierarchy For classification, the first task is to create the classes you require and (in this case) to insert the Nearest Neighbour Feature into each class. To create a class, you require the ‘Class Hierarchy’ window (shown in Figure 3.3) to be

open. If the window is not already visible, then click on the icon.

Figure 3.3: Class Hierarchy Window before inserting any classes.

To insert a class, right click in the Class Hierarchy window and select ‘Insert Class’ (Figure 3.4).

Figure 3.4. Inserting a class into the class hierarchy.

This provides you with an empty ‘Class Description’ (Figure 3.5).

Figure 3.5. Empty Class Description.

The next step is to edit your class description by first giving your class a name. For example, give the class the name “Water” and assign a blue

colour. When you have done this, insert and name new classes of “Forest”, “Other vegetation” and “Not Vegetation”. You should then have four classes inserted and named:

• Water • Forest • Other Vegetation • Not Vegetation

After giving each class a name, select an appropriate colour for each. This can be anything you wish, although the final classification will be easier to understand and interpret if you chose a logical colour (e.g., Green for Forest). Next, the features (e.g., mean object spectral response) to be used for classification (in this case, the standard nearest neighbour algorithm) need to be inserted into the class. To do this, right-click on the ‘and (min)’ and select ‘Insert new Expression’ (Figure 3.6).

Figure 3.6. Inserting a new expression into the class.

This will present the window (Figure 3.7), where you need to select ‘Standard Nearest Neighbour’ and click Insert.

Figure 3.7. Selecting the expression to be used for the classification.

Your resulting class description should be similar to that shown in Figure 3.8 for the forest class.

Figure 3.8. The resulting class description to be used for the classification.

The same procedure now needs to be repeated for the remaining three classes so that you end up with a classification hierarchy similar to that shown in Figure 3.9.

Figure 3.9 The final class hierarchy. To select the features used for the nearest neighbour classification use the ‘Edit Standard NN feature Space…’ function, Figure 3.10a, where initially you should just use the mean spectral values of the objects, Figure 3.10b.

a) The menu for editing the NN feature space

b) The dialog for selecting the features within the NN feature space

Figure 3.10. Editing the features used within the nearest neighbour classification. 3.4. Setting up the Process Tree 3.4.1 Segmentation As with all classifications in Definiens Developer, the first task is to perform a segmentation. In this case the use of a multi-resolution segmentation is recommended, although others could be investigated. As with previous units, it is recommend you create an outline within your process tree mirroring that outlined in Figure 3.11. Remember, a process is created by right-clicking in the process tree window and select ‘Append Process’ or ‘Insert Child Process’.




Note, that the layer weighting for the panchromatic band (PAN) has been increased to 2. This is in take advantage of the extra spatial resolution of the panchromatic band, 15 m rather than 25 m of the multispectral. 3.4.2. Classification To run the classification, you need to add a classification process to your process tree. This is achieved by right-clicking on the process you named ‘Classification’ and selecting ‘Insert Child Process’. Edit the new process such that it is similar in appearance to that shown in Figure 3.13. To select multiple classes, use the ‘Shift’ and ‘Control’ keys as you would in Windows Explorer.