Textural Infornation in SAR Images

Abstract

A multiplicative model was used to relate the image variance for a given land-use category to the individual variances associated with image speckle and target texture. Speckle was treated as a random process governed by signal fading and was considered to be statistically independent of the textural variations associated with the spatial variations of the scattering properties of visually "uniform" distributed targets. Seasat SAR imagery of Oklahoma was used to evaluate the textural autocorrelation function of five land-use categories: water, forest, pasture, urban, and cultivated. It was found that the maximum classification accuracy achievable using first-order statistics was 72 percent and that this level of accuracy was obtainable only by significantly degrading the spatial resolution in order to increase the number of independent samples per pixel. In contrast, second-order statistics-specifically, image contrast and inverse moment-provided a classification accuracy of 88 percent, with only a modest degradation in spatial resolution. A second study using SIR-A imagery of five forested regions has shown that the use of textural information can improve the classification accuracy among the five forest types from 75 to 93 percent.