Improving spectral results in a GTS context

Abstract

This paper shows the advantages of post-processing spectral classifications in a Geographical Information System (GIS) context in order to improve results. A maximum-likelihood algorithm was used to classify(both supervised and non-supervised) a Landsat TM sub-image in Central Mexico. Purely spectral processing yielded poor accuracy results, showing the spectral limitation to distinguish classes; as a consequence, merging classes was necessary in order to increase accuracy (from less than 55 to 82 per cent). GIS rules were finally applied based on ancillary data (terrain mapping units and elevation data) improving the final accuracy to 88.2 and 83.0 per cent (supervised and non-supervised classifications).