An application of including spatial information into a principle components analysis (PCA) image classification through an eigenvector spatial filter is given. The spatial filter is created as a linear combination of the chosen eigenvectors based on the spatial information in the image. These eigenvectors are extracted from the defined connectivity of the image pixels and are different from those eigenvectors used in conventional PCA. The surface
connectivity of the image is based on the binary matrix of image pixel neighbors (i.e. if <i>i</i> and <i>j</i> are neighbor pixels in an image and <i>i</i> does not equal <i>j</i> than matrix
entry <i>c<sub>ij</sub></i> = 1 other <i>c<sub>ij</sub></i> = 0). The proposed methodology is applied to an image example (i.e. Flightline C1) with 12 spectral bands from an airborne sensor as a selected
case study dataset. An explanation of how a spatial filter should be incorporated into PCA classification is given and two possibilities for gaining efficiency in the algorithm, through distributed computing and sampling is described. The advantages and drawbacks of both approaches, in terms of computing time, and amount of
variance accounted for in the image is discussed.