PCA – Principal Components Analysis

Principal Components Analysis – PCA – looks for variance in the multispectral data and re-orients the axes of the data to order the data in the direction of decreasing variance. At times, this can reveal difficult-to-see text by separating the low contrast information from the high contrast information.

PCA has two inputs, both of which are the same multiband image. The principal components are calculated from the region of interest specified in the “roi” image and the resulting PCA transform is applied to the “input” image. The two images must have the same number of bands, but they can be different sizes. There are three possible averages to analyze. The “variance” is the default. The three different averages can give slightly different results that might reveal different characters. One caution in using PCA, the more spectral bands that are included in the input images, the longer the computation time will be.

The number of output bands is equal to the number of input bands. The bands are ordered in decreasing values of statistical variance. As a result, the low order bands have higher contrast information. Erased text is often low contrast information and may be in higher order bands. The highest ordered bands are almost always random noise. If there are N output bands, they are numbered 0 to N-1.

In the “bandsOut” window, one can specify the number of output bands desired. If M bands are specified, then bands 0 to M-1 will be output.