A unified systolic architecture for artificial neural networks
Journal of Parallel and Distributed Computing - Neural Computing
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
MODTRAN on supercomputers and parallel computers
Parallel Computing
A software architecture for user transparent parallel image processing
Parallel Computing - Parallel computing in image and video processing
IEEE Transactions on Parallel and Distributed Systems
Heuristic Algorithms for Scheduling Iterative Task Computations on Distributed Memory Machines
IEEE Transactions on Parallel and Distributed Systems
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Parallel and Adaptive Reduction of Hyperspectral Data to Intrinsic Dimensionality
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
A dynamic earth observation system
Parallel Computing - Special issue: High performance computing with geographical data
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Parallel Implementation of Back-Propagation Algorithm in Networks of Workstations
IEEE Transactions on Parallel and Distributed Systems
On performance analysis of heterogeneous parallel algorithms
Parallel Computing
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
A robust framework for real-time distributed processing of satellite data
Journal of Parallel and Distributed Computing
Morphological associative memories
IEEE Transactions on Neural Networks
Efficient mapping algorithm of multilayer neural network on torus architecture
IEEE Transactions on Parallel and Distributed Systems
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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 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 high-dimensional (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.