Computing geostatistical image texture for remotely sensed data classification
Most classical mathematical algorithms for image classification do not usually consider the spectral dependence existing between a pixel and its neighbours, i.e., spatial autocorrelation. Thus, it would be advisable for discrimination of landcover classes to add to the radiometric bands of the sensor complementary information related to the textural features of an image, which can be analysed from the autocorrelation spatial structure of the digital numbers. In this way, the results obtained from pixel-by-pixel classifiers simultaneously taking into account both radiometric and texture information could be improved. This improvement would arise from the hypothesis that a pixel is not independent of its neighbours and, furthermore, that its dependence can be quantified and incorporated into the classifier. In this paper we present a methodology based on computing a set of univariate and multivariate textural measures of spatial variability based on several variogram estimators. Madogram and direct variogram for the univariate case, and cross and pseudo-cross variograms for the multivariate one, have been proposed. These measures are calculated for a specific lag of distance in a neighbourhood using a moving window on the two most representative principal components of the radiometric bands, enabling us to quantify the spatial variability of radiometric data at a local level. A computer program has been written to create a multiband image texture as output file that can be used within the classification process as additional information. An application of this methodology to lithological discrimination is presented using a Landsat-5 TM image.
spatial autocorrelation variogram estimators lithological discrimination texture analysis context information