Pixel Purity Index Assumes spectrally “pure” pixels are likely to correspond to “in scene” end members   For i = 1 to N Randomly generate a unit vector Vui Project image vectors (from n dimensional scatter plots) onto the vector Vui. i.e., V'ui · X = xi Identify the pixels that project at the extremes and increment those pixels 1 unit in the PPI image (i.e., min and max xi values) Next i Boardman, J.W., Kruse, F.A., Green, R.O. (1995). Mapping Target Signatures via Partial Unmixing of AVIRIS data, Fifth Annual JPL Airborne Earth Science Workshop, Vol. 1, AVIRIS Workshop, pp. 23-26 Chapter 5

Pixel Purity Index (cont’d) The output image is a brightness map of how often a pixel was defined to be an extreme. By thresholding the PPI image, we can identify pixels or clusters that are candidate end members. Chapter 5

Pixel Purity Index (cont’d) May choose to run this on Minimum Noise Faction (MNF) or noise adjusted principal component (NAPC) images to reduce the amount of data and to reduce the random variation in extremes due to noise in the data. Even though the selection is done using MNF data, we are only selecting pixel locations so further processing can use raw or calibrated data for the end member processing. Chapter 5

Pixel Purity Index (cont’d) Often a threshold is used to account for noise so you mark not only the extreme pixel as extreme, but pixels within ≈ 2 - 3 noise units so that you aren’t missing an end member due to noise in a similar pixel. See plots. Chapter 5

Pixel Purity Index (cont’d) Noise is defined to be 2 counts so accept values within say 4 counts of extreme   All pixels whose projections are shown are incremented one count in the PPI image. Note: if the MNF images are used, the threshold should usually be 2 since one count corresponds to one standard deviation. Chapter 5

Pixel Purity Index (cont’d) In ENVI there is a utility that plots the cumulative number of PPI pixels vs. iterations which should asymtote at a good value for N, so user can interactively terminate process. Chapter 5

Pixel Purity Index (cont’d) Plot the PPI data in either raw form or reduced form, e.g., MNF or PC and select pixels or clusters to define final end members. This normally requires interactive selection and the use of multiple projections of the n-dimensional data onto 2-D plots to define end members. This can be done with “Image Threshold to Region of Interest” and “n-Dimensional Viewer” in ENVI   Chapter 5

Pixel Purity Index (cont’d) Once the end members are defined in these projections, the corresponding pixels can be used to define the end member vectors from the means of the selected pixels in each cluster in either reflectance or radiance space. N.B. This process can be performed on full image sets or subsets instead of PPI derived data. However, the volume of data and the degree of confusion increases enormously. Chapter 5