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 utmost interest in remote sensing applications. Storage space, and mainly the computational effort required for classifying these kinds 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, classification based on regularized discriminant analysis (RDA) was compared with a number of non-parametric classifiers. Two synthetic image databases consisting of high-dimensional images were used for testing the performance of the classifiers. These datasets were created using a procedure proposed by the authors. The main conclusion of this paper is that RDA may be used successfully for classifying very-high-dimensional images with high-overlapping training sets. RDA also provides an excellent classification accuracy for classifying real datasets in which training sets are high-overlapping in the representation space.
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