Dimensionality reduction of hyperspectral images of vegetation and crops based on self-organized maps

Abstract

Hyperspectral images are multidimensional massive sets of information that have shown a great potential for different kind of applications as urban mapping, environmental management, vegetation and crops supervision and mineral detection. However, due to its high dimensional nature and the high variability of the spectral information, the dimensionality reduction process is one of the main challenges in processing hyperspectral images. The aim of dimensionality reduction is to eliminate redundant information and simplify the subsequent processes of classification and the search of information. In this context, several dimensionality reduction methods have been proposed, but most of them are not flexible enough to deal with the particular features of the hyperspectral images. In this way, the use of intelligent methods as neural networks and specially an unsupervised approach as self-organized maps, may improve the dimensionality reduction stage and the final classification process. This paper proposes an unsupervised method for the dimensionality reduction of hyperspectral images based on Kohonen self-organized maps, which, compared with other traditional methods such as principal component analysis (PCA) and wavelet decomposition, provides better classification results. The results provided in this paper use an RBF (radial basis function) classifier. On average, the proposed method provides a 64% dimensionality reduction and an 88.5% classification accuracy. These results suggest that the dimensionality reduction algorithm based on self-organized maps is an efficient approach compared with other popular algorithms. This is due to the ability of self-organized maps to automatically detect (self-organizing) relationships within the set of input patterns, which provides flexibility to deal with the special features of the hyperspectral images.