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SUPERVISED
CLASSIFICATION USINGSOFTWARE ERDAS IMAGIN
MUHAMAD FAZRUL SHAFIQ BIN ALIASMOHAMAD AKMAL BIN ABDUL RAZAK
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INTRODUCTION
Supervised classification is literally different from unsupervised classificationhuman guided classification instead of unsupervised which is calculated by t
Supervised classification is more accurate for mapping classes, but it depen
on the ability and skills of image specialist. However, the strategy is simple w
specialist must recognize conventional (real and familiar) or meaningful clas
scene related to their knowledge , such as personal experience with what is
the scene or by experience with thematic maps as well as by on-site-visits
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DIFFERENCE BETWEEN SUPERVISEDUNSUPERVISED CLASSIFICATION
SUPERVISED CLASSIFICATION UNSUPERVISED
CLASSIFICATIO
Based on the idea of users can select a region
in an image that are representative of specific
classes.
The user need to bound the region into one
group if the region is similar to other region. These bounds are depends on spectral
characteristic in the area usually its brightness
or strength of reflection.
User need to direct the image processing
software to use a testing sets or input classes
as reference for the classification based on
user knowledge
Software analysis at an image w
user providing sample classes
Software using its own tools or te
determine which the region are r
group them together The user only can specify which
software will use and desired nu
output classes but does not aid i
classification process
User also need to know the area
classified into groups of region w
characteristics that related to the
features on the ground.
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PROCEDURE
1. Open ERDAS IMAGINE and select the File tab. Click the Open folder and select r
from the menu
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2. Next, navigate to the folder on your hard drive that contains your imagery. In the ca
are opening an 8 band multispectral Ortho Ready 2A Geotiff of WorldView-2. Select the
and then click OK in the window.
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3. The image will then open and automatically load the True Color bands into the Red,
Blue guns. Under the Multispectral tab up in the menu under the bands section the us
different WV-2 band combinations. For the example below we selected the false color Iour supervised classification samples.
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AOI Tool
4. Once the wv-2 image is loaded the next step is to start collecting samples for classif
done using the AOI tool. To create an AOI go to the File Tab > New > 2D View > AOI Ltwo tabs will appear which is called Drawing and the other is Format.
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5. Click on the Drawing tab. Under the insert Geometry section click on the Polygo
6. Zoom into your first area to collect a sample using the zoom button and begin coll
first sample with multiple left clicks of the mouse. Double left click to close.
Collect at least three samples for each feature type so that you have a good represen
class type. For this example we are using an image collected over the burn region out
Boulder, Colorado. In this example, collect four different classes with three examples
:-
a) 3 samples from burned vegetation (in black in the false IR image),
b) 3 examples of healthy vegetation,c) 3 examples of man-made features,
d) 3 examples of soil
Each of these polygons will be saved to an AOI FILE.
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Signature Editor
7. Then, add these AOIs to a signature file for use as the training data set for supervis
classification. To open your signature file go to the Raster tab and click on the Superv
under the Classification section.
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8. Next, go into the AOI tab in the main viewer and click on the Select button and sel
AOIs and click on the Create new signature from AOI in the signature editor win
this for all three burn areaAOIs. Once three classes of burn have created, merge t
into one burn class with the aois spectral properties.
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8. Double left click on this new class1 and rename it to something representative of th
Burn Vegetation. You can then delete all the other classes that were input. Then, re
process for the other classes from the aoi.
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9. After the signature file are in their own classes, save the Signature File under the S
Editorwindow click on fileand save as. Navigate to the folder to save the signature f
a name. Then click OK.
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Supervised Classification
10. Under the raster tab click on Supervised and select Supervised Classification.
classification window will open. Then, select the input raster file in the supervised clas
window. Select the saved signature file and open the folder next to the Input Signatuthe .Sig file saved earlier. After that, select the folder to save the output classification f
Classified File and rename the .img file.
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11.Finally, colors of the classes can be change for better representation by click on the
classification.img in the contents window and open the Display Attribute Table. T
on the bottom of the viewer will appear. Find the classes and right click under the c
and select the colors.
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TAMMAT
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