Parallel Morphological Classification of Hyperspectral Imagery Using Extended Opening and Closing by Reconstruction Operations
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, many techniques for hyperspectral data classification 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 paper, we develop a new technique for joint spatial-spectral classification of hyperspectral image data which makes use of opening and closing by reconstruction, a kind of mathematical morphology operations which are extended here to hyperspectral images. A high performance parallel implementation of the proposed technique is also developed to satisfy time-critical constraints in remote sensing applications, using NASA's Thunderhead Beowulf cluster computer for demonstration purposes.