An application of spatial filtering techniques for land cover mapping using TM‐images

Abstract

Land cover classification using satellite imagery is commonly based on spectral information in the individual pixels. The information in neighbouring pixels is ignored. Spatial filtering techniques using information present in neighbouring pixels may however, contribute significantly to an improvement of the classification. In this study different methods of spatial filtering are applied to a part of a TM‐scene of Kenya to assess their relative reliability. The study area is characterized by extended, relatively homogeneous areas of eucalyptus forests and tea estates and by fragmentated areas of agricultural land use. Spectral information was combined with the results of different spatial filtering methods and then classified. The spatial filtering techniques applied were texture calculation by means of variance, “median minus original” filtering and fractal dimension computations using several sizes of templates. The obtained classification accuracy of several image combinations is compared using the percentage correctly classified and using an overall accuracy measure: the Kappa coefficient. It is concluded that in this case the spatial filtering techniques only slightly improve the classification. From the applied filtering methods texture calculation by means of variance yielded the best results.