User Manual - pancroma

Unsupervised ClassificationUnsupervised classification is a method for attempting to group similar pixels asa single hue with the intention of highlighting certain surface features, such asvegetation, water, urban areas, etc.RGB images may be classified. To classify an image, open an RGB image.Display it by selecting ‘Display Files’|’Display One Color Composite Image’.When the image is displayed, the ‘Classify’ menu selection under the ‘PostProcess’ category will be enabled. Select it. A data entry box will be displayedthat allows you to select from several algorithms and to set the classificationparameters. The defaults will usually work fine. The utility is has the followinglimitations:• Maximum input file size is limited to 16MB. This is because theclassification algorithm requires floating point pixel value precision.• The classification is computationally and memory intensive. It will take awhile to process an image.• It is not possible to easily predict how many iterations it will take the k-classification algorithm to converge on the solution. The progress barindicates the status of a single loop but it generally takes many loops toconverge.• The algorithm clusters pixels around a mean or median pixel value. As aresult the hues of the processed image will resemble those of theunprocessed image. It is not possible at this time to toggle the hues forhigher contrast but this will be available in future versions.The two images shown above illustrate unsupervised classification on an image.The image on the left is a SPOT® false color composite. The image uses131,000 unique colors. The image on the right has classified those colors into212