Automatic variogram parameter extraction for textural classification of the panchromatic IKONOS imagery

Abstract

Range and sill are two important parameters of a variogram. Their extraction usually involves experimental fitting of variograms using models specified by the analyst and requires much use of trial and error. The objective of this paper is to design an algorithm for extracting the range and sill of a variogram automatically without fitting a model. Combined with the semivariance at the lag of one pixel (γ1), the extracted range and sill were applied to the textural classification of a panchromatic IKONOS image over Xichang, Sichuan Province, China. Results show that any of these three parameters can lead to the increase of the classification accuracy. When all three parameters were used with the raw image data, the average kappa statistic for five window sizes increased from 0.24 to 0.76, indicating promise of the range and sill in texture classification.