A Comparison of Multispectral Image Classifiers Using High-Dimensional Simulated Data Sets

Abstract

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. Classical spectral classifiers may not be useful-even not valid- in practice to be used for classifying very high dimensional images. In this paper, we perform a comparative study of a number of spectral classifiers for classifying very high dimensional images. Our study concentrates on two synthetical image databases, created from scratch by using a procedure proposed by the authors. Dimensionality and spatial resolution of the images in the databases were selected in order to evaluate the performance of the classifiers. Key Words : Classification, Learning, Multispectral images, Remote Sensing. 1 Introduction By using very high spectral resolution scanners, it is possible to sample a wide range of the electromagnetic spectrum. Now, discrimination be..