Unsupervised Classification Erdas

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Unsupervised Classification by ERDAS IMAGINE

Aim: - To perform Unsupervised Classification

Introduction:-

Classification is an aspect of image processing in which quantitative decisions are made on the basis of the data present in the image, grouping pixels or regions of the image into classes representing different ground-cover types. The output of the classification stage may be regarded as a thematic map rather than an image (Gareth Rees, 1999). Classification techniques can be broadly divided into two types: supervised classification and unsupervised classification. Unsupervised Classification:

Unsupervised classification technique shares a common intent to uncover the major land cover classes that exists in the image without prior knowledge of what they might be.

Generically, such procedures fall into the realm of cluster analysis, since they search for cluster of pixels with similar reflectance characteristics in a multiband imagery.

They are also all generalizations of land cover occurrence since they are concerned with uncovering the major land cover classes, and thus tend to ignore those that have any low frequency occurrence.(IDRISI Manual)

This method is objective and data driven. It is particularly suited to images of targets or areas where there is no ground truth knowledge or where such information is not available.( Jian Guo Liu, Philippa J. Mason, 2009)

Procedure:-

Start ERDAS Imagine by click Start/ Programs/ ERDAS IMAGINE / ERDAS IMAGINE

To start unsupervised classification click on the Classifier icon on the ERDAS IMAGINE

icon panel.

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This will displays the Classification DBX

Click on the Unsupervised classification Tab, this will displays the Unsupervised

classification DBX

Select the input raster file for unsupervised classification by using file selector in the

input raster file:

Select the output raster file for unsupervised classification by using file selector in the input raster file:

In the clustering options, select the radio button Initialize from Statistics, and select the Number of classes to be created (say 50, we are going to create the maximum

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number of classes further we are going to club together in to optimum number of classes depending upon our need, this process is advisable because we are enable computer to classify and we are using our knowledge to made the classification as accurate as possible).

Click on the colour scheme options, it will open the Output Colour Scheme Options DBX, select Approximate True Colour radio button in order to get an colour output imageclose.

In the process options select the maximum number of iterations as 24, convergence

threshold is 0.95 and skip factor for X, Y as 1 Iteration stands for maximum number of times that ISODATA clustering can

re-cluster the data to avoid running the ISODATA utility too long in a cycle without reaching the convergence threshold. If the utility does not reach 0.95 thresholds, maximum 24 iterations can be performed.

The convergence threshold is the maximum percentage of pixels whose cluster assignments can go unchanged between iterations. This threshold prevents the ISODATA utility from running indefinitely. By selecting a convergence threshold of .95, we specify that as soon as 95% or more of the pixels stay in the same cluster between one iteration and the next, the utility should stop processing. In other words, as soon as 5% or fewer of the pixels change clusters between iterations, the utility stops processing

Skip factor specifies how may number of pixels to be skipped it is a less accurate process, if we taken as a one all pixels would consider for process.

Check the option if you want to classify zero values.

Click OK in the unsupervised classification DBX.

Click OK in the modular in order to complete the process.

Now Open the Raw image ex: germtm.img in the Viewer and open the output

Unsupervised Classified image ex: un_sup_germtm.img on the top of it.(You Check the number of layers opened in Viewer by Ctrl+L)

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ViewerMain Menu barRasterAttributesRaster Attributes Editor DBX opens

Raster Attributes Editor DBX Main Menu barEditColumn PropertiesColumn

Properties DBX opensSelect OpacityClick on Up tab to made Opacity on topmade Color second and Class-name as third in order as belowClick OK

Select the Column Opacity right click formula

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Formula DBX opens-put cursor in Formula boxenter 0Apply (This made the

Top layer of Unsupervised Classification invisible)

Now click on the first row which contains the Class1 made opacity as 1 and change

colour to yellow.

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Zoom to the Highlighted featuresViewerMain Menu barFlickerViewer Flicker DBX opens Click on Manual Flicker to made visible or invisible the features highlighted to conclude what is that feature in this viewer we can conclude that the class1 represents water bodies.

Now change the Class-name as water BodyChange the colour to BlueFollow this method to other classes also.

Now we have to combine the classes having same name into a single land use/land

cover classes.

For this click on Image interpreter in Erdas ribbonGIS AnalysisRecode.

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Recode DBX opensselect the input file ex: un_sup_germtm.img give output file name ex: unsupervised_classi.img click on “Setup recode” tab Thematic recode DBX opens.

In Thematic recode DBX select the land classes having same name ex: water by using mouse (by pressing shift + select each one or data on first start class and middle button on the last class or by simple dragging in the value column) now enter number “1” in the new value box click on Change selected rowsrepeat this step for all land use/land cover classesOK.

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Then hit Ok in Recode DBX after successful run of the modeller hit OK

Now go to the vieweropen the recent output file i.e., unsupervised_classi.img now go to the main MenuRaster AttributesRaster Attribute Editor DBX opens.

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In Raster Attribute Editor Menu barEditClick on Add Class Names

Now a column of “Class Names” added in the attribute table now by using Opacity option identify the land use/land cover class (as discussed above) give a proper name Also change the color Click on save in Attribute editor DBXfor reference see below.

Now we have an unsupervised image in our hand for further analysis

Thank you

Unsupervised Classification Erdas

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