Performing object-based image analysis - PCI Geomatics · 2017-06-06 · PCI Geomatics Page 7 Introduction About Object Analyst In Geomatica Focus, you can use Object Analyst, an

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Introduction 7 About Object Analyst 7 Workflow of OBIA 7 Project architecture 11

Region of study, objective, data 13 Region of study 13 Objective 14

Segmentation and feature extraction 17 Segmentation is key 17 Performing segmentation 19 Feature extraction 23

Collecting and editing training sites 27 Training sites are key 27 Selecting a training vector layer and training field 30

Classification 35 Calculating features 35 Selecting the segmentation file and fields 35 Viewing a classification 37

Accuracy Assessment 41 Evaluating classification accuracy 41

Editing and improving a classification 45 No classification is perfect 45 Rule-based classification 45 Merging and reshaping objects 51

Attaching a representation file to a classification 53 Modifying and saving the representation file 53 Importing a representation file 54

Exporting the classification 55 Converting the classification to a raster 55 Creating a pseudocolor segment 57 Exporting the pseudocolor segment to geocoded RGB 58 Creating a GeoTIFF file 59

Notes 61

PCI Geomatics Page 7

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AAbboouutt OObbjjeecctt AAnnaallyysstt In Geomatica Focus, you can use Object Analyst, an object-based image analysis (OBIA) feature, to segment an image into objects for classification and analysis. It differs from the traditional pixel-based approach, which focuses on a single pixel as the source of analysis.

Like OBIA, Object Analyst is designed primarily for use with very-high-resolution (VHR) imagery; however, you can use Object Analyst with any imagery that meets the necessary criteria. That is, you can use imagery of lower resolution, of various resolutions, and that is in an input format supported by Geomatica Focus.

By working with objects, which are extracted through a segmentation process, analysis can be both simplified and made more sophisticated. That is, by using objects, human-visual interpretation of images can be augmented by using the software to do much of the preliminary work in creating and determining which shapes, of given sizes, textures, and so forth, are of interest.

WWoorrkkffllooww ooff OOBBIIAA The workflow of performing an OBIA begins with preprocessing your data. If you are using more the one satellite image, such as is used in this tutorial, you can merge them into a single PCIDSK file. By doing so, you can more easily apply operations like resampling or reprojection to make the data easier to work with. You can also add extra layers, such as vegetation indices, a DEM, or a layer representing an area of interest (AOI).

After preprocessing, you can then use Object Analyst to segment the data. Segmentation is the first step of the supervised OBIA process. It involves selecting a file and the layers it contains to perform segmentation. When a file contains many layers, you can achieve better results (better objects) by using a relevant subset.

Other than the selected layers, segmentation is controlled by three basic parameters: scale, shape, and compactness. To achieve segmentation that meets the objectives of your supervised classification, some experimentation may be necessary.

The objects (polygons) layer created by the segmentation is accompanied by an attribute table containing a unique identification number (ID) for every object.

Introduction Performing object-based image analysis

Page 8 PCI Geomatics

After segmentation, you next perform feature extraction. This involves selecting the source channel or channels from which to compute a series of features (statistical and geometrical) for each object in the segmentation layer. You can select which features to compute. The features are used later during supervised classification.

Next, you edit training sites. This consists of selecting a series objects representative of each land-cover class of interest.

To make editing training sites easier, consider the following:

Select approximatively the same number of training sites for each class.

Select a few representative training objects for each class. That is, selecting too many training objects for a class does not improve the classification accuracy; rather, it may degrade it. Conversely, the number of verification objects can be much greater and will improve the reliability of the accuracy assessment report.

During supervised classification, you select one of two supervised classifiers: the maximum likelihood (MLC) or the Support Vector Machine (SVM) classifier. Each uses as input a selection of calculated features and one training-site field. The output is stored in a new field, and the output classification is displayed automatically with its legend in Focus.

You next evaluate the output classification by creating an accuracy-assessment report and by visually inspecting the output classification using the original image or ancillary data. If the classification is suitable, you can export it. If the classification is not entirely satisfactory, you can do any of the following:

Reform Shapes (A) Use this operation to improve the aesthetic of your classification. You can choose from two options: Automatic dissolve and Interactive edits. Each option modifies the shape of the object and the total number of objects. Therefore, the extracted features (statistical and geometrical) will no longer be accurate for the edited objects. It is best to use Reform Shapes at the end of your classification process or use it on a copy of your segmentation file.

Rule-based Classification (�� This operation refines an existing supervised classification by reassigning the class of some objects based on conditions or ranges. You can define and apply a classification rule that you have created on segments classified already or on unclassified segments. The prerequisite is the feature extraction of each segment. After algorithmic classification, you can either remove a class for some or all segments, or change the membership of certain segments in a class to improve the overall accuracy of the classification. Another scenario is when no algorithmic classification is performed and you want to assign certain segments to a class based on criteria you specify. You can create an attribute field in a vector layer to store the class information and create a rule by using the available extracted features.

Run another supervised Classification (C) You can run a second supervised classification by selecting a different set of features, different training-sites field (see next item), or both if your project contains more than one.

Performing object-based image analysis Introduction


Modifying an existing training-site field or create a new one (D) When a classification is particularly unsatisfactory and cannot be improved by rule-based classification, it is likely that the you will have to modify the training-sites field by: a) Modify the training site of a particular class by deleting an existing training site or selecting new ones; b) create a new existing land-cover class, or c) remove an existing land-cover class. Alternatively, instead of modifying an existing training field, you can create a new one to allow for easier analysis that will ultimately lead to a satisfactory classification.

Extract new features (E) Sometimes it may be necessary to extract new features before rerunning the supervised classification.

Run a new segmentation (F) If after several attempts a satisfactory classification has not been produced, you can run a new segmentation. If you do so, you must also rerun Feature Extraction, Training Sites Editing, and supervised Classification.

Note You can neither export nor transfer training and verification sites from one segmentation to another.



OOBBIIAA wwoorrkkffllooww ddiiaaggrraamm Figure 1 shows the general workflow of object-based image analysis (OBIA).

Figure 1. Workflow of OBIA

Performing object-based image analysis Introduction


PPrroojjeecctt aarrcchhiitteeccttuurree It is good practice to keep all relevant files of an OBIA project in the same folder (My_OBIA_project) and to link all files in a Geomatica project file (My_OBIA_project.gpr). By doing so, you ensure that the project is exportable to another location and can be reopened quickly, especially if a project contains many classifications with a legend.

A typical OBIA project contains an image and a segmentation file. The segmentation is performed on (all or a subset of) the image layers. The project can also contain ancillary data: raster or vector. While you cannot use ancillary-raster data for segmentation; that is, you can only use the layers in the image being segmented, you can use the ancillary-raster data later in feature extraction.

Figure 2. Folder organization of a typical OBIA project

The project will also typically contain an object field, either for training or accuracy assessment, from which a supervised classification is produced. The classification can often be improved by extracting additional features or by editing the object field.

A project can contain several segmentation files derived from the same image. Each segmentation is characterized by a different set of layers and parameters (scale, shape, compactness).

Each segmentation file contains a set features (statistical or geometrical) you specify, and which you can expand anytime during classification ( ). A segmentation can also contain several fields corresponding to a different set of training and accuracy objects, where the number of land-cover (or land-use) classes can vary and, therefore, will produce a different classification of the same area.

However, because the calculated features depend on the configuration of a particular segmentation, you cannot transfer training and accuracy objects from one segmentation to another.



Figure 3. Folder organization of a more complex OBIA project

Typically, segmentation and feature extraction are performed on the same image. However, you can perform segmentation and extract some features on one image and use another image to extract a second set of features. To do so, you must ensure that the second image contains the entire segmentation; otherwise, all objects outside the boundary of the second image will be set to zero.


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RReeggiioonn ooff ssttuuddyy The region of study (ROS) is centered in Ottawa (45°25′14.22′′N; 75° 41′47.30′′W), the capital of Canada.

The northern part of the ROS is rugged and hilly and is part of the Canadian Shield. This region is mostly forested (deciduous and mixed deciduous) with many lakes. Delimited by the Ottawa River, the southern part of the ROS is the Great Lakes-St. Lawrence Lowlands. Where the drainage is good, this region is mostly prime agricultural land while areas of poor drainage are mostly covered by a mix of wetlands and forested areas composed of tree species that support saturated soils.

Figure 4. Region of study (acquired September 5, 2016)1

In Figure 5, the ROS is shown in a Landsat-8 image. 1 Credit: Google Earth

Region of study, objective, data Performing object-based image analysis


Figure 5. Landsat-8 (p16r28 and p16r29) image of ROS (acquired August 26, 2016 (R: Band 6 | G: Band 5 | B: Band 4)

OObbjjeeccttiivvee The objective of this tutorial is to perform a supervised object-based classification to identify the following land-cover classes:

1. Agricultural areas

2. Urban areas

3. Forested Land

4. Water

5. Wetlands

However, these high-level land-cover classes do contain some heterogeneity and discriminating them is not a trivial task. This tutorial will demonstrate various strategies to achieve suitable results:

1. Use remote-sensing imagery acquired during various seasons to account for the dynamic nature of agricultural land and ease discrimination between coniferous and broad-leaf tree species.

2. Segmentation; that is, information generalization by regrouping pixels into meaningful objects.

3. Use a robust nonlinear classifier, such as Support Vector-Machine (SVM).

Performing object-based image analysis Region of study, objective, data


4. Separate some land-cover classes into subclasses to merge them later. The urban land cover is divided into two classes: urban dense and urban with vegetation.

5. Post-classification improvements.

Note In this tutorial, clouds and their shadows will be ignored.

In the following table, four Landsat-8 OLI images of the Ottawa region have been downloaded from the USGS Glovis website2.

No. Scene Path Row Acquisition date 1 LC80160282016127LGN00 16 28 May 6, 2016 2 LC80160292016127LGN00 16 29 May 6, 2016 3 LC80160282016239LGN00 16 28 August 26, 2016 4 LC80160292016239LGN00 16 29 August 26, 2016

Two mosaics, one for each acquisition date, were produced in Geomatica OrthoEngine. Because this was done only to reassemble an image previously split in rows for archiving, no color balancing was applied.

Band Name (wavelength) - spatial resolution Used for segmentation

Used for feature extraction

B1 Coastal/Aerosol (0.435-0.451 μm)-30m B2 Blue (0.452-0.512 μm)-30m √ B3 Green (0.533-0.590 μm)-30 √ B4 Red (0.636-0.673 μm)-30m √ √ B5 Near Infrared (0.851-0.879 μm)-30m √ √ B6 Short-wave Infrared-1 (1.556-1.651 μm)-30m √ √ B7 Short-wave Infrared-2 (2.107-2.294 μm)-30m √ B8 Panchromatic (0.503-0.676 μm)-15m B9 Cirrus (1.363-1.384 μm) 30m B10 Thermal Infrared -1(10.60-11.19 μm) -100m B11 Thermal Infrared-2 (11.50-12.51 μm) -100m

Each mosaic was reprojected to UTM-18T (WGS84) and subsetted with the following bounds:

Upper left: 372 285E; 5 078 025.000N

Lower Right: 479 565E; 4 979 475.000N

The final step was to merge the two subsetted mosaics into a single PCIDSK file for a total of 14 spectral channels. This file, L8_Ottawa_20160506_20160826.pix, is provided with this tutorial.

2 http://glovis.usgs.gov/


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SSeeggmmeennttaattiioonn iiss kkeeyy The success of an object-based supervised classification starts with a good segmentation. Unfortunately, there are no objective rules to follow or absolute criteria to tell if a segmentation is good or not. As a guideline, a trade-off is often necessary between the mean size of objects (generalization) and their homogeneity. That is, most objects should, in general, correspond to only one land-cover class and their shapes should align with the boundaries (edges) observed in the imagery.

Segmentation also depends on the selected layers (in this case, the spectral bands) and it is not mandatory to use all available layers.

Shape, scale, and compactness parameters are also assigned to the objects. Choosing a good combination increases the success of the supervised classification and requires some experimentation.

The following series of figures shows the results of various combinations of scale (SC), shape (SP) and compactness (CP) values from the segmentation of the B6, B5, and B4 bands (followed by the total number of objects) for May 6 and August 26, 2016.

SC:25, SP:0.10, CP:0.5

(3 356 129) SC:50, SP:0.10, CP:0.5

(589 186) SC:100, SP:0.10, CP:0.5

(136 928)

Segmentation and feature extraction Performing object-based image analysis


SC:25, SP:0.25, CP:0.5

(2 608 795) SC:50, SP:0.25, CP:0.5

(493 238) SC:100, SP:0.25, CP:0.5

(139 528)

SC:25, SP:0.50, CP:0.5

(1 003 221) SC:50, SP:0.50, CP:0.5

(338 648) SC:100, SP:0.50, CP:0.5

(97 889)

SC:25, SP:0.75, CP:0.5

(662 681) SC:50, SP:0.75, CP:0.5

(182 387) SC:100, SP:0.75, CP:0.5

(57 007)

SC:25, SP:0.80, CP:0.5

(531 987) SC:50, SP:0.80, CP:0.5

(150 372) SC:100, SP:0.80, CP:0.5

(44 572)

Performing object-based image analysis Segmentation and feature extraction


SC:25, SP:0.90, CP:0.5

(212 739) SC:50, SP:0.80, CP:0.5

(83 488) SC:100, SP:0.80, CP:0.5

(24 599)

PPeerrffoorrmmiinngg sseeggmmeennttaattiioonn In this step, you will perform segmentation on a Landsat image.

Figure 6. Operation list with Segmentation selected

To perform segmentation 1. In Focus, open the Landsat image provided with this tutorial

(L8_Ottawa_20160506_20160826.pix).

2. On the Analysis menu, Object Analyst. The Object Analyst window appears.

3. In the Operation list, select Segmentation.

4. Under Source Image Layers, click Select. The Layer Selection window appears



5. In the File list, select L8_Ottawa_20160506_20160826.pix, and then select the spectral bands, as shown. That is, because the objective of this tutorial is to classify the high-level land cover classes based on imagery acquired at two different seasons (spring and late summer), the spectral bands that emphasize the vegetation changes for these two seasons will be used.

6. After you select the bands, click OK.

7. In the Object Analyst window, under Parameters, enter values as follows:

Scale: 50 Shape: 0.8 Compactness: 0.5

8. Under Output, click Browse, and then enter the path and file name to which to write the segmentation result. That is, enter the path and file name as follows:

~\OBIA_Tutorial_Ottawa\L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix

9. Click Add and Run . The operation is added under Process Canvas and the segmentation process begins.



Figure 7. Object Analyst window with Segmentation set up to run

After the process is complete, the segmentation layer will appear in Focus displayed over the Landat-8 image, as shown in Figure 8.

Figure 8. Segmentation layer in Focus

The segmentation layer contains the following four fields:

ShapeID - unique value for every object

Area (sq m)



Perimeter (m)

PixelValue

VViieewwiinngg tthhee sseeggmmeennttaattiioonn llaayyeerr iinn AAttttrriibbuuttee MMaannaaggeerr You can view information about the segmentation layer in Attribute Manager.

To view the segmentation layer in Attribute Manager 1. On the Files tab in Focus, expand the

L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix file.

2. Right-click the vector layer ([VEC]), and then click Attribute Manager.

CCrreeaattiinngg aa GGeeoommaattiiccaa pprroojjeecctt ffiillee Create a Geomatica project file that will contain the Landsat-8 image, the segmentation layer, and, later, the classification result.



To create and save a Geomatica project file 1. On the File menu, click Save Project.

2. Enter the path and file name as follows: ~\OBIA_Tutorial_Ottawa\Ottawa_OBIA.gpr

FFeeaattuurree eexxttrraaccttiioonn In this step, you will perform feature extraction.

Figure 9. Operation list with Feature Extraction selected

The term feature, in object-based image analysis (OBIA), corresponds to an attribute representing some information about the image objects. In Object Analyst, the same concept is used. Different characteristics of image objects are referred to as features, or attributes, of a segment.

You can compute two types of features:

Statistical

Geometrical

Statistical features are computed based on the image pixels inside an object. Features are computed for each of the selected image bands and added to the attribute table of the vector segment layer as new fields (attributes). The minimum, maximum, the mean, and the standard deviation are available.

Geometrical features that represent geometric characteristics of an object (polygon segment) make object-based image analysis (OBIA) advantageous over pixel-based analysis. In Object Analyst, the geometric features are computed by analyzing the polygon boundary created during segmentation, so raster information is not required. Object Analyst computes many shape descriptors used commonly, such as compactness, elongation, circularity, and rectangularity.

In this tutorial, the mean of all spectral bands will be calculated for every object. This will be sufficient for a general land-cover/land-use classification.

To perform feature extraction 1. In the Object Analyst window, under Operation, select Feature

Extraction.

2. Under Source Channels, click Select. The Layer Selection window appears.



3. Select L8_Ottawa_20160506_20160826.pix as the input file, and then select the bands to use. If necessary, in the Band Alias column, you can modify the alias by clicking the corresponding box, and then entering a new alias. The new band alias will appear as the corresponding fields in the attribute table. In the following example, new band alias has been specified to include information about the acquisition date.

4. After you specify any new band aliases, click OK.

5. Under Segmented Vector Layer, click Select, and then specify L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix as the input segmentation file.

6. Click OK.

7. Under Feature Attributes, select the Mean check box, but leave the other check boxes clear.

8. Click Add and Run . The operation is added under Process Canvas and the feature-extraction process begins.



Figure 10. Object Analyst window with Feature Extraction set up to run

After the process is complete, you can view the selected statistics for image L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix in Attribute Manager.

Figure 11. Attribute Manager showing selected statistics for image L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix


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TTrraaiinniinngg ssiitteess aarree kkeeyy The next operation is to collect and edit training sites. With a supervised classification, this is perhaps the most crucial operation and can be labor intensive.

You can use the following series of figures as a guide to help you to collect training and verification classes for each land-cover class.

Class No. May 6, 2016 August 26, 2016 Class

/description

1

Wetlands - Marsh

1

Wetland - Peatlands

Collecting and editing training sites Performing object-based image analysis



/description

2

Forest - coniferous

3

Forest - deciduous

4

Water

Performing object-based image analysis Collecting and editing training sites



/description

5

Urban - dense

6

Urban –with vegetation

7

Agriculture – bare (in May)




/description

8

Agriculture –vegetation (in May)

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Figure 12. Operation list with Training Sites Editing selected

To select a training vector layer 1. In the Object Analyst window, under Operation, select Training Sites

Editing.

2. Under Training Vector Layer, select a segmentation file. In this case, select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix.

3. Under Training Sites, click Edit. The Training Sites Editing window appears.

4. Beside Training field, click Create, and then in the Training Field window, type T_A_SVM1b as the name of the field in the Field Name box. The new field is added to L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix.

To create a new class and select training or accuracy objects 1. In the Training Sites Editing window, click Add Class.

2. In the Class Name column of the table, enter a class name, and then in the Color column, select a color. The color will be used for the classification, but you can change it later in the process.



3. Beside Sample type, click Training.

4. In Focus, pan, zoom, or both, as necessary to find an area representative of wetlands.

5. Click Individual Select , click an object in the image, and then in the Training Sites Editing window, click Assign. You can select multiple objects to assign by holding down the Shift key and clicking each object you want. You can also drag a selection square or rectangle over the objects you want.

6. In the Training Sites Editing window, beside Sample type, click Accuracy assessment, and repeat step 5 to select objects for accuracy assessment. Note: You cannot use the same object simultaneously for training and as a verification object. If an object is already assigned to a class, and you select it again, it will be updated with the new state. The same rule applies to a class selected previously.



7. Repeat steps 1 to 6 for each class.

Tip Save your project regularly.

In Attribute Manager, the Training field of L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix is now updated for all objects that have been selected for training (Class Name_T) of accuracy assessment (Class Name _A).

Figure 13. Training field updated in Attribute Manager



Figure 14. Training site with classes defined

Color R-G-B Class

255-102-0 Wetlands

0-153-0 Forest - coniferous

0-250-0 Forest - deciduous

0-0-255 Water

0-0-0 Urban - dense

74-74-74 Urban - vegetation

255-204-255 Agriculture - bare

204-51-204 Agriculture -vegetation



Figure 15. Position of training and verification objects used in this tutorial


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CCaallccuullaattiinngg ffeeaattuurreess The next operation is to classify the data using some calculated features, statistical or geometrical, in combination with a field containing training (Class Name_T) and accuracy-assessment (Class Name_A) objects.

Note You can use the L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix image provided with the tutorial.

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Figure 16. Operation list with Classification selected

To run classification 1. In the Operation list, select Classification.

2. Under Vector Layer and Fields, click Select, and then in the Vector Layer and Field Selector window, do the following:

Select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix as the segmentation file.

Select the extracted feature fields to use for the classification. Click OK.

3. Under Type, click Supervised.

4. Under Training Field, select the field of the segmented layer with the training and accuracy objects (Training).

5. Under Output Class Field, type SVM_T1 as the name of the field to which to write the classification result. The field will be added to the segmentation file in Attribute Manager.

6. Under Classifier, click SVM.

Classification Performing object-based image analysis


7. Click Add and Run . The operation is added under Process Canvas and the classification process begins.

8. After the classification process is complete, save your project file (Ottawa_OBIA.gpr).

In Attribute Manager, you can view the three new fields that have been added for L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix:

Label: Unique integer label assigned automatically to each class by OBIA.

SVM_T1: Result of the classification.

SVM_Voting_Prob: SVM voting probability. This field can be used to assess the robustness of the classification.

Figure 17. Attribute Manager showing the three new fields added

Performing object-based image analysis Classification


VViieewwiinngg aa ccllaassssiiffiiccaattiioonn After the classification process is complete, a legend appears on the Maps tab in Focus. The color of each class corresponds to those specified during selection of the training and verification objects. The opacity is set at 25 percent for quick interpretation of the results.

To modify the styles of your classification 1. Double-click Agriculture_bare.

The Style Selector window appears. Each class is composed of two parts, the object interior color (Polygon – Fill) and its contour (Line – Solid). Switch between Advanced and Simple modes to view all the available options to modify the class style. Note: You may need to first click More.

2. Click Advanced, and then set the class opacity to 100.

3. To preview the change, click Apply, and then to accept the change, click OK. Changes to styles are saved automatically.

4. Repeat steps 1 to 3 for each class by setting the opacity at 100.



You can also remove the outline of a classified object by, in Advanced mode, selecting 2 in the Part list, and then clicking Remove .

Figure 18. Sample supervised classification

Performing object-based image analysis Classification


The following series of images show the progression of the image through object-based image analysis (OBIA).

Landsat-8 May 6, 2016. R: Band 6 G: Band 5 B: Band 4

Landsat-8 August 26, 2016. R: Band 6 G: Band 5 B: Band 4

Supervised classification result (detail) With object contour



Supervised classification result (detail) Without object contour


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EEvvaalluuaattiinngg ccllaassssiiffiiccaattiioonn aaccccuurraaccyy You can evaluate the accuracy of a classification by switching between the classification and the source imagery. You can also, if necessary, create an accuracy report.

Figure 19. Operation list with Accuracy Assessment selected

To create an accuracy report 1. Under Operation, select Accuracy Assessment.

2. Under Classified Vector Layer, select the segmentation file that contains a classification result from a supervised classification; that is, select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix.

3. Under Classification field, select the field that contains the classification result; that is, select SVM_TI.

4. Under Reference field, select the field that contains the training and accuracy objects used to generate the selected classification field; that is, select Training.

Accuracy Assessment Performing object-based image analysis


5. Click Generate Report. The Accuracy Assessment Report window appears.

The Accuracy Assessment Report window has three tabs:

Sample Listing: Shows all assessed samples, with georeferenced position, image coordinates, classified value/name, and reference value/name.

Error (Confusion) Matrix: Shows a matrix of all classes between reference data and classified data.

Accuracy Statistics: Shows various accuracy statistics, such as overall accuracy, kappa coefficient, and confidence intervals.

Performing object-based image analysis Accuracy Assessment


After you create an accuracy-assessment report, you can export it as either a Microsoft Excel spreadsheet (.xls) or as a text file (.txt).

To export an accuracy-assessment report

1. In the Accuracy Assessment Report window, on the Sample Listing, Error (Confusion) Matrix, or Accuracy Statistics tab, as applicable, click Export Report. The File Selector window appears.

2. In the File Selector window, select a folder, enter a file name for the report, and then click Save.

Figure 20. Sample accuracy-assessment report


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NNoo ccllaassssiiffiiccaattiioonn iiss ppeerrffeecctt Even if a high accuracy has been achieved, it is unlikely that the classification is perfectly suited to the project.

Typically, before exporting a classification refinement of some land-cover classes or some manual editing might be necessary.

While Object Analyst offers several options for refining a classification, this section will focus on two:

Rule-based classification

Merging and reshaping objects

RRuullee--bbaasseedd ccllaassssiiffiiccaattiioonn You can use rule-based classification to split a class into subclasses.

In addition to supervised and unsupervised classification algorithms, with Object Analyst you can also create a custom rule to assign class membership to segments. By creating a custom rule, you can as an analyst select the criteria that determines membership of a sample in a class based on your understanding of the domain, data, or both.

You can use various tools or assessments to better understand the data and processes, but the decision on membership of a class is human-made. You can then create a classification rule by using the available features and based on your understanding of the data and the application domain. The knowledge to construct a classification rule comes from existing understanding, which is translated simply in the form of an equation. This process is highly user-dependent and involves data exploration and onscreen interpretation of both the image and the segments.

Object Analyst can define and apply a classification rule that you have created on segments classified already or on unclassified segments. The prerequisite is the feature extraction of each segment. After algorithmic classification, you can either remove a class for some or for all segments, or change the membership of certain segments in a class to improve the overall accuracy of the classification.

A close inspection of the classification created in Classification has revealed that the wetland class is too general; it would be preferable to split this class into two subclasses:

Editing and improving a classification Performing object-based image analysis


Wooded wetland, mostly coniferous (dense to sparse tree cover)

Open wetland (no trees)

To perform this task, you will use the interactive Pick Range feature of the Rule-based Classification operation.

To perform class reassignment by using Pick Range 1. Under Operation, select Rule-based Classification.

2. Under Class Edit, click Assign.

3. Under Vector Layer, select the segmentation file with the classification field to modify; that is, select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix.

4. Under Parameters, do the following:

In the Class field list, select the field with the classification to modify; that is, select SVM_T1.

In the Class filter list, select the land-cover class to modify; that is, select Wetlands.

In the New class list, select the class to which to write the objects. This can be an existing class or a new class; however, you will create a new class by typing Wetland_open.

5. To perform the interactive class reassignment, select the Specify condition check box, and then click Pick Range. The Feature Visualization window appears.

Performing object-based image analysis Editing and improving a classification


Fortunately, the near infrared band is sufficient to allow a good discrimination between the wooded and open wetlands.

To guide your range selection, in Focus, open the wetland vector map from Natural Resources Canada, which is provided in this tutorial.

To open the wetland vector map 1. In Focus, on the File�menu,�click Open, and then select

Wetlands_Canvec_50K.pix.

2. In the Feature Visualization window, drag the Minimum value and Maximum value sliders to compare the values. Notice that the selected objects appear in a different color in Focus.

3. Optionally, you can deselect the other classes to help you to find the best range because only the wetland class will be modified.

The following series of images show the variations of the minimum and maximum values.

In Figure 21, the selected objects are highlighted in pale orange. These are the candidates of the new open wetland class. Notice that the wetland reference data is now displayed with a thicker white outline on the polygon so they are more easily differentiated from the objects classified as wetlands.



Figure 21. Sample new wetland class

In Figure 22, the selected objects are highlighted in pale orange. These are the candidates of the new open wetland class; however, too many objects are selected.

Figure 22. Sample new wetland class (too many objects selected)

In Figure 23, the selected objects are highlighted in pale orange. This is a good range.



Figure 23. Sample new wetland class (good range)

After you find a suitable range, in the Feature Visualization window, click OK.

You can now apply your changes to the classification:

In the Object Analyst window, click Add and Run .

Figure 24. Object Analyst window with Pick Range

In Attribute Manager, the SVM_T1 field is updated with the new Wetland_open class.



Figure 25. Attribute Manager showing updated SVM_T1 field

The new class has also been added to the legend.

Figure 26. Class added to legend



MMeerrggiinngg aanndd rreesshhaappiinngg oobbjjeeccttss With Reform Shape, you can improve the aesthetic of your classification.

Figure 27. Operation list with Reform Shape selected

You can choose from two options:

Automatic dissolve: Merges two adjacent polygons based on class membership

Interactive edits: Merge or split polygons you select

Caution Only reform shapes after classification and after you have made a backup of your project. Reforming shapes modifies object shapes and the total number of objects, which renders the extracted features (statistical and geometrical) invalid.

MMeerrggiinngg ppoollyyggoonnss The selected layer must have one or more polygons with class information

To merge adjacent polygons 1. Under Operation, select Reform Shapes.

2. Under Type, click Interactive edits.

3. Under Vector Layer, select the segmentation file with the classification field you want to modify; that is, select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix.



4. Under Editing Tools, click Individual Select ( ), and then in Focus, click the first polygon you want to merge.

5. Click Merge Polygon ( ), and then in Focus, click one or more polygons to merge. To undo a merge, on the File menu, click Undo. You can also press Ctrl+Z. The following image shows merging before and after.

6. After you have merged the polygons you want, on the Maps tab, right-click the map layer, and then click Save.


AAttttaacchhiinngg aa rreepprreesseennttaattiioonn ffiillee ttoo aa ccllaassssiiffiiccaattiioonn

MMooddiiffyyiinngg aanndd ssaavviinngg tthhee rreepprreesseennttaattiioonn ffiillee The styles you apply to a classification are saved in the Geomatica project file (*.grp). If the segmentation file that contains the classification field is opened standalone in Focus or in another project the styles will be unavailable.

To retain your styles for future use, create a representation file (*.rst). You can do so at any stage of your project.

To modify and save the representation file 1. In Focus, on the Maps tab, right-click the vector layer with the

classification result you want, and then click Representation Editor. The Representation Editor window appears.

2. To modify the style of a class, in the Style column of the table, double-click the class. The Style Selector window appears.

3. Select a style, and then click Apply.

4. Repeat steps 2 and 3 for each class you want to modify, and then click OK.

Attaching a representation file to a classification Performing object-based image analysis


5. To save the representation file, on the toolbar, click Save ( ). The Save As window appears.

6. In the File box, you can type or select the name you want; that is, select SVM_T1.

7. In the Description box, type a brief description to help identify the relevance of the file.

IImmppoorrttiinngg aa rreepprreesseennttaattiioonn ffiillee With a project in which you want to use the representation you created in Modifying and saving the representation file, you can import the file. That is, when you open a classification without its associated Geomatica project file (*.gpr), you can import the representation file you created previously.

To import a representation file 1. In Focus, open the classification.

2. On the Maps tab, right click the classification, and then click Representation Editor.

3. In the Attribute list, select the field with the supervised class labels; that is, select SVM_T1.

4. On the toolbar, click Link ( ). The RST Link window appears.

5. In the File list, select the file you created previously; that is, select Classif_SVM_T1.rst, and then click OK.

6. In the Representation Editor window, click Apply, and then click OK. The styles are now associated with the classification you selected.


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CCoonnvveerrttiinngg tthhee ccllaassssiiffiiccaattiioonn ttoo aa rraasstteerr While Geomatica features various methods by which you can export a classification to another format, this tutorial describes using the POLY2RAS algorithm to convert the classification to a raster file using a class label field. You then create a pseudocolor table (PCT) and assign the various colors to each class.

To convert the classification to a raster layer 1. In Focus, on the Tools menu, click Algorithm Librarian.

The Algorithm Librarian window appears.

2. Open the POLY2RAS algorithm. The POLY2RAS Module Control Panel window appears.

3. Under Input Ports, click Browse, and then select L8_Ottawa_SEG_B6B5B4_50_0.8_0.5.pix as the input polygon layer and bounding area.

4. Under Output Ports, type a path and SVM1_classif.pix as the file name for the output file.

Exporting the classification Performing object-based image analysis


5. Click the Input Params 1 tab, and then do the following:

In the Output type list, select Raster. In the Field name box, type Label.

6. Click Run. The classification you specified is converted to a single-channel, 8-bit file.

Performing object-based image analysis Exporting the classification


CCrreeaattiinngg aa ppsseeuuddooccoolloorr sseeggmmeenntt The output of POLY2RAS is a gray-valued, 8U single channel. The numeric values correspond to the values stored in the specified POLY2RAS field name.

Figure 28. Numeric values in Geomatica Focus

To create a pseudocolor segment 1. In Focus, on the Files tab, right-click the image channel you want, point

to View, and then click As Pseudocolor.

2. Click the Maps tab, and then expand the pseudocolor file ( ).

3. Double-click the square beside class 1 ( ), and then in the Change PCT Entry Color window, click a new (RGB) color for the class, and then click OK.

4. Repeat step 3 for classes 2 through 8, selecting the color you want for each class.

5. Right-click the pseudocolor file, and then click Save As. The Save As window appears.

6. Save the pseudocolor segment to the PCIDSK file that contains the classification.

Exporting the classification Performing object-based image analysis


EExxppoorrttiinngg tthhee ppsseeuuddooccoolloorr sseeggmmeenntt ttoo ggeeooccooddeedd RRGGBB A pseudocolor segment is now attached to the classification. When you open the classification in Focus, it will display with the colors you selected.

You can now export the file to a geocoded RGB file by running the PCE algorithm.

To encode the single channel pseudocolor classification to an RGB file 1. In Focus, open the Algorithm Librarian, and then open the PCE

algorithm. The PCE Module Control Panel window appears.

2. For Input: Layer to Encode, select the 8U channel containing the classification.

3. For InputPCT: Pseuodocolor Layer Table, select the PCT you created previously.

4. For the output file, enter the path and SVM1_Classif_RGB.pix as the file name.

Performing object-based image analysis Exporting the classification


5. Click Run.

CCrreeaattiinngg aa GGeeooTTIIFFFF ffiillee After you create the new RGB file, you can export it to a GEOTIFF file by using the Translate tool in Focus.

To create a geoTIFF file 1. In Focus, click the File menu, point to Utility, and then click Translate.

The Translate (Export) File window appears.

2. For Source file, select the RGB file you created previously.

3. For Destination file, select a path and SVM1_Classif_RGB.tif as the file name.

4. Under Output format, select TIF: TIFF 6.0.

5. Under Source Layers, select all three 8U raster channels, and then add them to the Destination Layers list by clicking > Add >.

6. Click Translate.


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Performing object-based image analysis - PCI Geomatics · 2017-06-06 · PCI Geomatics Page 7 Introduction About Object Analyst In Geomatica Focus, you can use Object Analyst, an