A comparative study of techniques for hyperspectral image classification

Abstract

Hyperspectral images are very important in many Earth Observation programs. The large amount of information is contained in hyperspectral images (hundreds of narrow and continuous spectral channels) is very useful for applications in which the characterization of the Earth surface materials is relevant. This is due to the fact that each observed element can be uniquely characterized by its spectral signature, for instance in precision agriculture, urban planning or detection/prevention of natural disasters, among others. However, the large dimensionality of hyperspectral images represents a challenge for analysis algorithms, both from the storage and processing viewpoints, resulting from data variability and correlation. Several algorithms have been proposed in the literature for the analysis of hyperspectral images. In this paper, we review the most popular techniques for hyperspectral classification. These techniques are inter-compared using three publicly available hyperspectral data sets.