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Last Post 20 Mar 2011 07:59 AM by  anon
Apply identical classification to multiple images?
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anon



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20 Mar 2011 07:59 AM
    I'd like to apply a classifier developed on one image, image1, to a different image, image2, without training the classifier on image2. That is, apply the classifier (be it MLC, NN, SVM, etc) to image2 as if it were image1. For example, if I believed that there were no spectral differences between the two images, then I should not have to collect training sites and retrain the classifier on image2, I should just be able to directly apply the classifier trained on image1 and expect the same classification accuracy. I cannot figure out a way to accomplish this in ENVI. In IPW there were classification routines that returned the classifier itself, which could then be applied to any other images, provided the input bands were the same. Does anyone know how to do this in ENVI? Best, Josh

    MariM



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    21 Mar 2011 02:49 PM
    One way you can apply the training data statistics from one image to another is via a statistics file. To do this, open the ROIs and in the ROI tools, choose Options ->Calculate Covariance with Stats. Then select all of your ROIs and select the 'Stats' button which will plot the mean ROI spectra and calculate all of the necessary statistics for running a classifier. In the ROI statistics tool, go to File ->Save all ROI results to an ENVI statistics file. Now the training data statistics are in a file that can be used on another dataset. The only way to apply a pre-calculated statistics file in a classification is to use Endmember collection from Spectral ->Mapping Methods menu. Select the new file you want to classify. This brings up the Endmember Collection dialog. Select the algorithm you want to use under the Algorithm menu (notice there is no SVM or NN - these are not applicable for this method of classifying). Then go to Import ->From Stats File and open your ROI stats file you calculated previously. Once the spectra are imported, you can select Apply to continue through the classification.

    Deleted User



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    24 Mar 2011 11:10 AM
    Thank you very much, that is helpful. Cheers, Josh
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