neroherbal.blogg.se - Add multiple images mosaic pro erdas

#ADD MULTIPLE IMAGES MOSAIC PRO ERDAS PATCH#
#ADD MULTIPLE IMAGES MOSAIC PRO ERDAS FULL#

Click on the Classifer button located in the main menu bar.Repeat steps 6 thur 9 for the other classes.īased upon what you discovered when evaluating the location and pattern of the unsupervised clusters you may wish to remove or combine some clusters from the signature file.The flashing pixels are the pixels in this class, click on the Class_Name in the editor and give it meaningful name and assign it a meaningful color.Now the dialog opens, so turn on the auto mode. From the viewer menu bar select Utility | Flicker.Change the opacity for class 1 in the Cell Array to 1 and press return, this class will be shown in the viewer.

#ADD MULTIPLE IMAGES MOSAIC PRO ERDAS PATCH#

Now change the color for class 1 to something like Yellow or red so it is easier to see, click on the color patch and change the color.

In the Raster Attribute Editor, click on the word OPACITY at the top of the Opacity column, then right-click-hold on the word Opacity and select formula from the column option menu, click on the "0" (the zero on the number pad) and click on Apply.

Start by setting opacity for all classes to "0".

NOTE: you can edit the way this editor appears by selecting Edit | Column Properties.

select Raster | Attributes from the viewer, the editor will be displayed.

You will now go through the steps to allow you to view the "true color", or false color image while also viewing the unsupervised clusters. This will add two data layers into a single viewer (one on top of the other).

from the viewer tool bar open a classified raster layer (this could be - or should be - the unsupervised classified data layer you just created), make sure the "clear display" option is turned off from the "raster options tab" found from that dialog box.

All you are going to do is display the classified product "on top" of the unclassified image to help determine what surface material the various self-organized clusters relate to.

4,5,3 - this can be either the original "un-clipped" image of the mosiac image) it doesn't matter.

file|open|raster layer and display your multi bands combination (ie.

Click OK on the dialog and on the status dialog when the process is completed.

Stop processing when 95% of the pixels stay in the same cluster between interations)

Set the maximum iterations up to 40 (this insure the process does not run on forever).

Try 3 times the number of expected classes)

Enter the number of different NUMBER OF CLASSES (ie.

Initial clusters can be generated either arbitrarily or from an existing signature mean select initial from statistic.

NOTE: you should output a signature file.

Input raster file (*.img from the moasic step) and provide an output name.

Select Unsupervised classification, the dialog opens.

With ERDAS running click on the classifier icon from the icon panel.

Unsupervised clustering on representative data img file with all your clipped images included.

Click EDIT and ADD IMAGE or use the button.

Once you have all your clipped images, you will want to put them backtogether for your unsupervised classification. img file that contains the area, layers, and data type you selected.

Fill out the options selecting the image input and output, and click on FROM INQUIRE BOX button.

Click the DATAPREP button, and select SUBSET IMAGE.

Move the box and enlarge it include the area you want to clip.

Click on UTILITY and select INQUIRE BOX.

Start with the image you want to clip in a viewer.

The method that I have used and found to work the fastest is the following:

There are multiple ways to clip an image, many of them use the subset command. Below are the basic steps in more detail. There are more then one path through this approach.

#ADD MULTIPLE IMAGES MOSAIC PRO ERDAS FULL#

Fourth, all signatures either from the unsupervised or supervised approach are appended together to form a single signature file which is used to "classify" (as a supervised classification step) the complete and full extent of the originals image. Third, if the unsupervised approach to forming signature clusters fails to distinguish a land or sea surface material which "YOU KNOW IS PRESENT", you may add to the signature file the result of a "supervised" clustering approach. Second, these mosaiced images are allowed to "self-orgainized" themselves into spectral clusters using an unsupervised approach. Remember that the hybrid approach is designed to let you first work with smaller subsets of the image that have been clipped and mosaiced together to form a selection of the image heterogeneity for a given theme (such as forest, or urban, or water). Follow the steps below for an example of a "single" path through the steps of the Hybrid Approach