Combination of neural and statistical algorithms for supervised classification of remote-sensing image

Various experimental comparisons of algorithms for supervised classi®cation of remote-sensing images have been reported in the literature. Among others, a comparison of neural and statistical classi®ers has previously been made by the authors in (Serpico, S.B., Bruzzone, L., Roli, F., 1996. Pattern Recognition Letters 17, 1331±1341). Results of reported experiments have clearly shown that the superiority of one algorithm over another cannot be claimed. In addition, they have pointed out that statistical and neural algorithms often require expensive design phases to attain high classi®cation accuracy. In this paper, the combination of neural and statistical algorithms is proposed as a method to obtain high accuracy values after much shorter design phases and to improve the accuracy±rejection tradeo€ over those allowed by single algorithms. Ó 2000 Elsevier Science B.V. All rights reserved.