Spatial/Spectral analysis of hyperspectral image data

Abstract

The integration of spatial and spectral responses in hyperspectral image data analysis has been identified as a desirable objective by the remote sensing community. However, most available attempts are based on the consideration of spectral information separately from spatial information, and thus the two types of information are not treated simultaneously. In this paper, we describe our background in applying joint spatial/spectral techniques for full (pure)- and mixed-pixel classification of hyperspectral image data. Most of the techniques described in this work are based on classic mathematical morphology theory, which provides a remarkable framework to achieve the desired integration. The performance of the proposed methodologies is demonstrated by comparing them to other well-known pure- and mixed-pixel classifiers, using both simulated and real hyperspectral data collected by the NASA/JPL-AVIRIS and DLR-DAIS 7915 imaging spectrometers.