Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data

A geostatistical contextual classifier for land-cover mapping using remote sensing data is presented. To integrate spatial information with spectral information derived from remote sensing data, a geostatistical indicator approach is adopted to determine the probability of a certain land-cover class occuring at an unsampled location given that any other land-cover classes occur at neighboring locations. The geostatistical indicator algorithm applied here is simple indicator kriging with local means. This approach can directly integrate both spatial information of ground data (hard data) and spectral information of remote sensing data (soft data) within an indicator kriging framework. This algorithm is applied to the classification of multi-sensor remote sensing data for land-cover mapping. This classification result is compared with a result obtained from a conventional spectral information based classification method.