Title
Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms
Abstract
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.
Year
DOI
Venue
2008
10.1007/s10586-007-0048-1
Cluster Computing
Keywords
Field
DocType
Heterogeneous computing,Parallel performance,Neural networks,Hyperspectral imaging,Mathematical morphology
Data mining,Computer science,Massively parallel,Symmetric multiprocessor system,Real-time computing,Artificial intelligence,Earth observation,Artificial neural network,Computer vision,Mathematical morphology,Workstation,Hyperspectral imaging,Heterogeneous network
Journal
Volume
Issue
ISSN
11
1
1386-7857
Citations 
PageRank 
References 
4
0.61
21
Authors
5
Name
Order
Citations
PageRank
Javier Plaza156158.04
Rosa Pérez244345.46
Antonio Plaza33475262.63
pablo martinez461758.77
David Valencia519416.07