Parallel spatial-spectral processing of hyperspectral images

Abstract

Hyperspectral image processing has been a very active area in remote sensing and other application domains in recent years. Despite the availability of a wide range of advanced processing techniques for hyperspectral data analysis, a great majority of available techniques for this purpose are based on the consideration of spectral information separately from spatial information information, and thus the two types of information are not treated simultaneously. In this chapter, we describe several spatial-spectral techniques for dimensionality reduction, feature extraction, unsupervised and supervised classification, spectral unmixing and compression of hyperspectral image data. Most of the techniques addressed in this chapter are based on concepts inspired by mathematical morphology, a theory that provides a remarkable framework to achieve the desired integration. Parallel implementations of some of the proposed techniques are also developed to satisfy time-critical constraints in remote sensing applications, using NASA's Thunderhead Beowulf cluster for demonstration purposes throughout the chapter. Combined, the different topics covered by this chapter offer a thoughtful perspective on future potentials and emerging challenges in the design of robust spatial-spectral techniques for hyperspectral image analysis. © 2008 Springer-Verlag Berlin Heidelberg.