Authors: Javier Plaza Rosa Pérez Antonio Plaza Pablo Martínez David Valencia
Publish Date: 2007/11/29
Volume: 11, Issue: 1, Pages: 17-32
Abstract
The wealth spatial and spectral information available from lastgeneration Earth observation instruments has introduced extremely high computational requirements in many applications Most currently available parallel techniques treat remotely sensed data not as images but as unordered listings of spectral measurements with no spatial arrangement In thematic classification applications however the integration of spatial and spectral information can be greatly beneficial Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters lowcost heterogeneous networks of computers HNOCs have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions In this paper we develop a new morphological/neural algorithm for parallel classification of highdimensional hyperspectral remotely sensed image data sets The algorithm’s accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms using two networks of workstations distributed among different locations and also a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center in Maryland
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