Statistical spatial filtering: application to aerial photographs

Abstract

Spatial filtering is shown to apply not only to the identification of deterministic signals but also to the classification of images. The spectral content of images is put into a classifier that extracts the dominant eigenvectors responsible for statistical features. Principal images that carry most of the information are obtained by using optical representations of eigenvectors as spatial filters. The statistical stability and the intrinsic dimensionality of Fourier spectra are related to the fast estimation of useful eigenvectors.