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

Various experimental comparisons of algorithms for supervised classificatio n of remote-sensing images have been reported in the literature. Among othe rs, a comparison of neural and statistical classifiers has previously been made by the authors in (Serpico, S.B., Bruzzone, L., Roll, F., 1996. Patter n 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 alg orithms often require expensive design phases to attain high classification accuracy. In this paper, the combination of neural and statistical algorit hms is proposed as a method to obtain high accuracy values after much short er design phases and to improve the accuracy-rejection tradeoff over those allowed by single algorithms. (C) 2000 Elsevier Science B.V. All rights res erved.