MultiSpec Tutorial: Unsupervised Classification (Cluster Analysis)
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1 MultiSpec Tutorial: Unsupervised Classification (Cluster Analysis) Requirements: MultiSpec application and image titled ag020522_dpac_cd.lan. Two Clustering algorithms are available in MultiSpec. They are useful in grouping similar pixels in the image into clusters or categories. One algorithm implemented is a simple one-pass type. The second is an iterative type called ISODATA. We will use the ISODATA algorithm for this tutorial. To start this tutorial, be sure that the ag020522_dpac_cd.lan image that was used in tutorial 1 is open. Also clear any selections in the image window by striking the Delete Key A cluster analysis will be run using the image file represented by the active (top-most) multispectral image window. 3.1 From the Processor menu, select Cluster to bring up the cluster specifications dialog box. Select Do Not Save in the Cluster Stats: popup menu. Select Cluster mask file and select Image window overlay under the Write Cluster Report/Map To group. This will cause a cluster map to be created as a thematic image disk file and displayed as an overlay on the multispectral image window. Revised 9/10/2015 Page 1 of 5
2 MultiSpec Tutorial 3 Unsupervised Classification (Cluster Analysis) 3.2 Then select ISODATA This will cause the ISODATA Specifications dialog box to be displayed. Enter 7 for the number of clusters, 100 for the convergence percentage and set the line and column intervals to 1 (if needed) and then select OK. 3.3 You are now back to the Cluster Specifications dialog box. Select OK to close this dialog box and start the clustering operation. You will be prompted to enter a name for the cluster map disk file and where to save the file. Just use the defaults by selecting OK in the Save Cluster Map dialog box. A cluster map will now be created with around 7 classes in an unsupervised manner. You will notice the colors change in the image window as the pixels are sorted into cluster classes during each iteration. After the final iteration, a thematic image file with a map of the cluster classes will be saved to disk. The text output for the cluster operation will be at the end of text output window. The information includes the mean values for each of the channels for each cluster for both the intial condition and the ending condition. If the map information is available for the image, the final area for each cluster is listed in the units specified in the coordinate view for the image window. Usually the convergence is set for a little less than 100 so that the process does not take too long to complete. We used 100 in this example so that you have a chance to watch the pixels change cluster classes which illustrates the nature of the ISODATA algorithm. The cluster map overlay on the multispectral image window will look similar to the following. Page 2 of 5 Revised 9/10/2015
3 Unsupervised Classification (Cluster Analysis) MultiSpec Tutorial 3 You can turn the overlay on and off by using the Red O popup menu button in the toolbar to the right of the small mountain zoom button. 3.4 Now open the cluster map thematic image file. This will be the same image as is shown in the overlay above but you will have more control over the cluster classes. From the File menu, select Open Image to bring up the open image dialog box. You may have to change the Files of type popup to All Files or Thematic Files. Then select ag020522_dpac_cd_clmask.gis and then select OK. One may need to select Thematic for the Files of Type popup menu. The Thematic Display Specifications dialog box to the right will be displayed. The default settings are fine; select OK in the Display Thematic Image dialog box. This opens a thematic image window. Page 3 of 5 Revised 9/10/2015
4 MultiSpec Tutorial 3 Unsupervised Classification (Cluster Analysis) 3.5 The cluster class legend is on the left in the thematic image window below. Compare the Cluster map with the original image window. Note clusters that represent trees, sparse vegetation, thicker vegetation, different soil colors and shadows. There are several things one can do to evaluate the image. One can move the cursor over a color chip, hold the shift key down (cursor will change to an open eye) and click the left mouse button down and up to cause the colors for that class to blink off and on (alternate between white and the color). If one holds down both the shift and ctrl keys and then click the left mouse button down and up, then all of the other classes will blink off and on. These procedures are helpful in understanding the extent of the classes in the image. One can also change the class color by double clicking on the color chip. One can change the cluster class names by double clicking on the name to the right of the color chip. (The list of interpreted cluster names that I come up with are: Light soil, Medium colored soil, Sparse vegetation (weeds), Dark soil, Dense vegetation (trees, wheat, weeds), Shadowed trees, Image blank. 3.6 One can also group the cluster classes together in information groups by selecting Classes/Groups in the popup menu above the legend. Then drag the cluster classes into similar information group categories. Again one can double click on the group name to change the name. For example one could change the appearance of the thematic image to represent Bare Soil, Vegetation and Non-image informational classes. The popup in the legend allows one to display the original cluster (spectral) classes. Page 4 of 5 Revised 9/10/2015
5 Unsupervised Classification (Cluster Analysis) MultiSpec Tutorial 3 Concluding Remarks There are many other operations that one can do with MultiSpec including several Reformatting processes. See other tutorial or the MultiSpec Introduction at the MultiSpec web site for more information. The MultiSpec web site is: Or contact Larry Biehl at biehl@purdue.edu with questions. Page 5 of 5 Revised 9/10/2015
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