The performance of regularized discriminant analysis versus non-parametric classifiers applied to high-dimensional image classification
International Journal of Remote Sensing
Classi® cation of very-high-dimensional images is of the utmost interest in remote sensing applications. Storage space, and mainly the computational e ort required for classifying these kinds of images, are the main drawbacks in practice. Moreover, it is well known that a number of spectral classi® ers 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 classi® cations when the training sets are
... 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 classi® er that may adjust the decision boundaries in a better fashion. To do so, classi® cation based on regularized discriminant analysis (RDA) was compared with a number of non-parametric classi® ers. Two synthetic image databases consisting of high-dimensional images were used for testing the performance of the classi® ers. 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 classi® cation accuracy for classifying real datasets in which training sets are high-overlapping in the representation space.