A comparative study of statistical and neural methods for remote-sensing image classification and decision fusion
This paper focuses on evaluating a number of statistical and neural methods for supervised, pixel-wise remote-sensing image classification and decision fusion. Despite the enormous progress in the analysis of remote sensing imagery over the past three decades, still much is desired in the area of image classification as no specific algorithm is known to provide accurate results under all circumstances. Decision fusion may be pursued to combine the outputs of different classifiers applied on the same data, in the hope of combining the best of what each approach provides. We report the results of the comparison between several classification and fusion methods on two real datasets, one of which is the standard benchmark Satimage dataset. It is shown that the fusion approaches can indeed outperform the performance of the best classifier.