Geostatistics and remote sensing: an improvement in image classification

In the context of the use of remote sensed data for monitoring land cover it is very important to develop methodologies to obtain reliable maps. In order to achieve this objective a possible approach is to combine both “spectral” and “spatial” features to characterizing each ground class. In this paper we propose the integration of a spectral classifier for remote sensed data at medium resolution, based on a traditional statistical supervised classifier as “Maximum Likelihood”, with the spatial information provided by a geostatistical tool, as “Indicator Kriging” algorithm. Using this combined approach, better results in land cover class discrimination have been obtained and the resulting maps look more homogenous than in the case with the spectral information only.