"Ensembles of Neural Networks for Soft Classification of Remote Sensing Images"

ABSTRACT: In recent years, the remote-sensing community has became very interested in applying neural networks to image classification and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a “panacea”. In this paper, we propose the use of “ensembles” of neural networks as an alternative approach based on the exploitation of the complementary characteristics of different neural classifiers. Classification results provided by neural networks contained in these ensembles are “merged” according to statistical combination methods. In addition, the use of ensembles formed by neural and statistical classifiers is considered. Experimental results on a multisensor remotesensing data set are reported that point out that the use of neural networks ensembles can constitute a valid alternative to the development of new neural classifiers “more complex” than the present ones. In particular, we show that the combination of results provided by statistical and neural algorithms provides classification accuracies better than the ones obtained by single classifiers after long “designing” phases