AliaKim.Co Unsupervised Classification 1 UiTM AliaKim.Co Remote Sensing Introduction An unsupervised classification algoritm is derived by modeling observed data as a mixture of several classes that are described by linear combinations that estimates the data density in each class by using parametric nonlinear functions. When applied to the image, the algorithm will learn efficient codes for the images that capture the statistically significant structure difference in the image. In addition to specifying the desired number of classes, the analyst may also specify parameters related to the separation distance among the clusters and the variation within each cluster. Unsupervised classification is not completely done without human involvement. However it does not start with a pre-determined set of classes as in a supervised classification. Objectives 1. Learn how to conduct the procedure of unsupervied classification using ERDAS. 2. To define the cluster and processing options. 3. To display output image. 4. To label classes on a dataset that has been output from ISODATA.
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UiTM AliaKim.Co Remote Sensing
Introduction
An unsupervised classification algoritm is derived by modeling observed data as a mixture of several
classes that are described by linear combinations that estimates the data density in each class by
using parametric nonlinear functions. When applied to the image, the algorithm will learn efficient
codes for the images that capture the statistically significant structure difference in the image. In
addition to specifying the desired number of classes, the analyst may also specify parameters related
to the separation distance among the clusters and the variation within each cluster. Unsupervised
classification is not completely done without human involvement. However it does not start with a
pre-determined set of classes as in a supervised classification.
Objectives
1. Learn how to conduct the procedure of unsupervied classification using ERDAS.
2. To define the cluster and processing options.
3. To display output image.
4. To label classes on a dataset that has been output from ISODATA.
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Procedure
1. Click the Classifier icon in the ERDAS IMAGINE icon panel to start the Classification utility.
2. The Classification dialog will open and click the Unsupervised Classification to perform an
unpervised classication using the ISODATA algorithm.
3. In the Input Raster File, navigate to file pkl210291rso.img and under the Output Cluster
Layer, enter the name for the output file pkl210291rso_uns.img
4. Uncheck the Output Signature Set.
5. Enter the Class Number to 14 classes and set the iteration to 10.
6. Click OK.
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Click the Classifier icon in the ERDAS IMAGINE icon panel to start the Classification utility.
The Classification dialog will open and click the Unsupervised Classification to perform an
unpervised classication using the ISODATA algorithm.
Input Raster File, navigate to file pkl210291rso.img and under the Output Cluster
Layer, enter the name for the output file pkl210291rso_uns.img.
Uncheck the Output Signature Set.
Enter the Class Number to 14 classes and set the iteration to 10.
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Click the Classifier icon in the ERDAS IMAGINE icon panel to start the Classification utility.
The Classification dialog will open and click the Unsupervised Classification to perform an
Input Raster File, navigate to file pkl210291rso.img and under the Output Cluster
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7. Open a viewer and Open to Raster Layer the output file. Then open its Attributes.
8. Open the Attribute’s Column Properties and r
Names, Color, Opacity and followed by the rest of columns.
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Open a viewer and Open to Raster Layer the output file. Then open its Attributes.
Open the Attribute’s Column Properties and rearrange the column from the first three
, Color, Opacity and followed by the rest of columns.
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Open a viewer and Open to Raster Layer the output file. Then open its Attributes.
earrange the column from the first three Class
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9. Edit the Class Names by changing the Color box of the class and observe the Viewer for the
changes. Identify the feature that change in color and specify it in the related class name.
Repeat the process until it is done to all the classes.
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Class Names by changing the Color box of the class and observe the Viewer for the
changes. Identify the feature that change in color and specify it in the related class name.
Repeat the process until it is done to all the classes.
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Class Names by changing the Color box of the class and observe the Viewer for the
changes. Identify the feature that change in color and specify it in the related class name.
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10. If needed for an exact classification, regroup the class name that are relatable and make it
into on single color or if the user wish to look for the smallest features classification, the
Flicker Utility can be use.
Open a new viewer and open the Raster Layer.
In the Select Layer To Add
the Clear Display box.
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t classification, regroup the class name that are relatable and make it
into on single color or if the user wish to look for the smallest features classification, the
Flicker Utility can be use.
Open a new viewer and open the Raster Layer.
Select Layer To Add,select the unsupervised image and in the Raster Options,
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t classification, regroup the class name that are relatable and make it
into on single color or if the user wish to look for the smallest features classification, the
Raster Options, uncheck
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11. In the viewer, select View and click Arrange Layers. In the Arrange Layers dialog, rearrange
the image, the coloured image on the top while the black and white in at th
Then select the Utility and select Swipe.
dialog – left to right.
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In the viewer, select View and click Arrange Layers. In the Arrange Layers dialog, rearrange
the image, the coloured image on the top while the black and white in at th
elect Swipe. The image can be swipe by controlling it in the viewer swipe
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In the viewer, select View and click Arrange Layers. In the Arrange Layers dialog, rearrange
the image, the coloured image on the top while the black and white in at the bottom.
by controlling it in the viewer swipe
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12. Open the Interpreter, select GIS analysis and followed by Recode.
Insert the unsupervised input file and rename the output file. Then click the Setup Recode button.
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, select GIS analysis and followed by Recode.
Insert the unsupervised input file and rename the output file. Then click the Setup Recode button.
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Insert the unsupervised input file and rename the output file. Then click the Setup Recode button.
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Select the classes as stated in the previous attribute that want to be recoded and change it to the
new value by selecting the New Value and click Change Selected Rows.
Then click OK in the Thematic Recode and
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Select the classes as stated in the previous attribute that want to be recoded and change it to the
alue by selecting the New Value and click Change Selected Rows.
Then click OK in the Thematic Recode and followed by OK in the Recode dialog.
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Select the classes as stated in the previous attribute that want to be recoded and change it to the
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Now it can be seen that the Attribute of the recode image is just in 5 classes.
Rearrange all the column back into a better presentation by adding the Area and Class Names
Column and arrange the column accordingly in the Column Properties.
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Now it can be seen that the Attribute of the recode image is just in 5 classes.
ack into a better presentation by adding the Area and Class Names
arrange the column accordingly in the Column Properties.
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ack into a better presentation by adding the Area and Class Names
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13. Then in the Image Interpreter/ GIS analysis
recoded image and rename for the output Clump image. Then click OK.
14. Next select Eliminate. Insert the clump image and rename for the Eliminate image output
file. Then click OK.
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Then in the Image Interpreter/ GIS analysis – select Clump. Insert the unsupervised and
recoded image and rename for the output Clump image. Then click OK.
Next select Eliminate. Insert the clump image and rename for the Eliminate image output
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Clump. Insert the unsupervised and
Next select Eliminate. Insert the clump image and rename for the Eliminate image output
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Open the image in the new viewer. The image will be in black and white.
Raster/Attributes. In the Attributes table, add the Class N
Colour Column just like the previous and rearrange the column accordingly in the Column Properties.
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Open the image in the new viewer. The image will be in black and white.
Raster/Attributes. In the Attributes table, add the Class Names and Area Column, then recolour the
Colour Column just like the previous and rearrange the column accordingly in the Column Properties.
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ames and Area Column, then recolour the
Colour Column just like the previous and rearrange the column accordingly in the Column Properties.
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Now the differences can be seen between the two images. The left image is before Clump and
Eliminate process while the right image is after the Clump and Eliminate process.
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Now the differences can be seen between the two images. The left image is before Clump and
le the right image is after the Clump and Eliminate process.
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Now the differences can be seen between the two images. The left image is before Clump and
le the right image is after the Clump and Eliminate process.
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15. On the latest image, select AOI(A
The image is clearly seen being disrupted by clouds
dialog, select the Polygon tool and select the disrupted area.
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On the latest image, select AOI(Area Of Interest) and click Tools.
The image is clearly seen being disrupted by clouds – thus it shows falls informations.
dialog, select the Polygon tool and select the disrupted area.
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formations. In the AOI
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Then click the Display AOI Styles icon. Check the Fill and select the colour similar to the colour of the
targeted area.
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Then click the Display AOI Styles icon. Check the Fill and select the colour similar to the colour of the
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Then click the Display AOI Styles icon. Check the Fill and select the colour similar to the colour of the
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Now the area of interest are free from the error due to the clouds.
area that were affected by clouds.
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Now the area of interest are free from the error due to the clouds. Repeat the process for the other
area that were affected by clouds.
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Repeat the process for the other
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16. Select the Composer/New Map Compositio
Width to 9 inches, Map Height 12 inches and the Display Scale is 1:1.
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omposer/New Map Composition. Give the name for the map and set the Map
Width to 9 inches, Map Height 12 inches and the Display Scale is 1:1.
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n. Give the name for the map and set the Map
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Select the icon and make a box in the Map Composer, then select Viewer for the Map Frame Data
Source and click the image in the Viewer.
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Select the icon and make a box in the Map Composer, then select Viewer for the Map Frame Data
Source and click the image in the Viewer.
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Select the icon and make a box in the Map Composer, then select Viewer for the Map Frame Data
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In the Map Frame dialog, select Change Scale and Frame Area(Maintain Map Area), renumber the
Scale to 1:17000 and click the Use Entire Source button.
Then click the Grid icon, select the image and state the Spacing to 5000, click Copy to Vertical butt
and check the Use Full Grid.
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In the Map Frame dialog, select Change Scale and Frame Area(Maintain Map Area), renumber the
Scale to 1:17000 and click the Use Entire Source button.
hen click the Grid icon, select the image and state the Spacing to 5000, click Copy to Vertical butt
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In the Map Frame dialog, select Change Scale and Frame Area(Maintain Map Area), renumber the
hen click the Grid icon, select the image and state the Spacing to 5000, click Copy to Vertical button
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Then select the Legend icon. Select all the classes and Apply.
Select the Scale button and Center the Alignment and check the Kilometers for the Units. Apply.
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Select all the classes and Apply.
Select the Scale button and Center the Alignment and check the Kilometers for the Units. Apply.
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Select the Scale button and Center the Alignment and check the Kilometers for the Units. Apply.
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Then select the A button for the title or other text that want to be added.
KLANG – REMOTELY SENSED FEATURES.
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Then select the A button for the title or other text that want to be added. The title for the map is
SENSED FEATURES.
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title for the map is
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Then select the (+) icon and click on the image. Then
icon.
In this dialog, click the Symbol Style and change it to the North Arrows
symbol and resize it.
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k on the image. Then select the Display Style icon
In this dialog, click the Symbol Style and change it to the North Arrows. Choose the
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icon and click on the +
. Choose the North Arrow
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Lastly, rearrange all the additional information for the map and save.
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Lastly, rearrange all the additional information for the map and save.
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Questions:
1. Perform unsupervised classification using the default option by the software.
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Perform unsupervised classification using the default option by the software.
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Perform unsupervised classification using the default option by the software.
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2.Perform unsupervised classification using different bands combination.
Select Interpreter/Utilities and Layer Stack.
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2.Perform unsupervised classification using different bands combination.
Select Interpreter/Utilities and Layer Stack.
Insert the image and rename the
output file image. Select Layer number
1 – Add, 2-Add, 3-Add.
Then click OK.
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Insert the image and rename the
output file image. Select Layer number
Add.
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Open new Viewer/File/Open/Raster Layer
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Insert the image and rename for the output and uncheck the Output Signature Set
of Classes to 14 and the iteration to 10.Then click OK.
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Open new Viewer/File/Open/Raster Layer and open the image. Select Classifier and choose
age and rename for the output and uncheck the Output Signature Set
of Classes to 14 and the iteration to 10.Then click OK.
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and open the image. Select Classifier and choose
age and rename for the output and uncheck the Output Signature Set. Set the Number
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Open new Viewer/Open/Raster Layer
Then open the Attributes for comparison purpose.
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Open new Viewer/Open/Raster Layer – the unsupervised image.
ibutes for comparison purpose.
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Band Combinations: 1,2,3 (The Layer Stack
The unsupervised image.
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Layer Stack image)
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In the Attributes, after recolour. The information is seen like below.
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In the Attributes, after recolour. The information is seen like below.
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Band Combinations: 4,5,6(The Layer Stack Image)
The unsupervised image.
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(The Layer Stack Image)
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In the Attributes, after recolour. The information is seen like below.
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In the Attributes, after recolour. The information is seen like below.
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Band Combinations: 1 ,2 ,3,4,5,6
The unsupervised image.
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,4,5,6(The Layer Stack Image)
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In the Attributes, after recolour. The information is seen like below.
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In the Attributes, after recolour. The information is seen like below.
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3.Comment on the output given by the different band combinations.
Band 1,2,3 respectively shows very unusual results. It mix all the features together forming an
uninterpretable features information. Next for the band 4,5,6 respectively shows such a familiar and
accurate features informations. For the band 1,2,3,4,5,6 respectively shows similar results as in
bands 4,5,6. Therefore it also can be consider to be accurate information.
4.Which band combinations give a reliable result?
The band combinations that give a reliable result are band 4,5,6 and 1,2,3,4,5,6. This is because both
of the band combinations shows an accurate presentations of features. Therefore they are both can
