INTEGRATION OF ANCILLARY DATA INTO A MAXIMUM-LIKELIHOOD CLASSIFIER WITH NONPARAMETRIC PRIORS

The inclusion of prior probabilities derived from the frequency histograms of the training sets has already been demonstrated to significantly improve the performance of a maximum-likelihood classifier. Based on the same principles, a method is presently proposed to integrate the information of ancillary data layers (morphology, pedology, etc.) into the classification process. The statistical basis of this probabilistic approach is described along with a procedure for the preliminary estimation of the information content expressed by the ancillary data about the cover categories. A case study is then illustrated concerning a rugged area in Tuscany (central Italy) sensed by multitemporal Landsat Thematic Mapper (TM) scenes. Ground references of nine cover categories were collected and digitized together with four ancillary data layers (elevation, slope, aspect and soils). A maximum-likelihood classification with nonparametric priors based only on the TM scenes was first tested, yielding a kappa accuracy of 0.744. The ancillary data were then analysed and integrated into the modified classifier, with notable increases in classification accuracy (up to kappa = 0.910). It is concluded that the utility of such an approach must be evaluated in relation to the spectral separability of the cover categories considered and the information content of the ancillary layers.