Parallel Morphological/Neural Classification of Remote Sensing Images Using Fully Heterogeneous and Homogeneous Commodity Clusters

The wealth spatial and spectral information available from last-generation 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, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity cluster-based analysis of high-dimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center