The Performance of Regularized Discriminant Analysis Versus Non-Parametric Classifiers Applied to High-Dimensional Image Classification.

Classification of very high dimensional images is of the almost interest in Remote Sensing applications. Storage space, and mainly the computational effort required for classifying this kind of images are the main drawbacks in practice. Moreover, it is well known that a number of spectral classifiers may not be useful-even not valid- in practice for classifying very high dimensional images. Even if they are valid, they do not provide high accuracy classifications when the training sets are high-overlapping in the representation space due to the shape of the decision boundaries they impose. In these cases, it is preferable to adopt a classifier that may adjust the decision boundaries in a better fashion. To do so, we compare classification based on regularized discriminant analysis (RDA) with a number of nonparametric classifiers. We shall use two synthetical image databases, consisting of high-dimensional images for testing test the performance of the classifiers. These datasets have b..