Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis

In digital image processing of remotely sensed data, texture analysis, filtering, and edge detection techniques, among others, may be improved through the use of variable window sizes which extend the analysis beyond the immediate pixel to a larger geographic area. In this paper, semivariograms are used to generate geographic windows, which are customized to the scale of observation. Three examples are used to illustrate the improvements over the use of arbitrarily selected fixed geometric windows in remote estimation of forest inventory, forest structure characteristics, and in land-cover classification. A program to handle the semivariance calculations is described. The code was written in the C programming language under AIX-Unix on an IBM RISC 6000 24-bit color workstation to support a common pixel-interleaved digital image format, and has been tested on optical and radar remote sensing imagery in three mapping studies. Copyright (C) 1996 Elsevier Science Ltd