Communications of the ACM - Special issue on parallelism
Compiling collection-oriented languages onto massively parallel computers
Journal of Parallel and Distributed Computing - Massively parallel computation
Vector models for data-parallel computing
Vector models for data-parallel computing
Implementation of a portable nested data-parallel language
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
The CM-5 Connection Machine: a scalable supercomputer
Communications of the ACM
Parallel Analysis of Clusters in Landscape Ecology
IEEE Computational Science & Engineering
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Evaluating the DIPORSI Framework: Distributed Processing of Remotely Sensed Imagery
Proceedings of the 8th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
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Biological diversity is decreasing at an alarming rate worldwide. Improved ecosystem monitoring can help detect problems in time to intervene. Earth orbiting satellites, collecting terabytes of imagery daily, can support effective monitoring of many habitats. Data parallelism is ideal for many automated image analysis algorithms, but less natural for the complex spatial structure of most ecosystems. This paper presents a coarse-to-fine processing framework based on a set of spatial transformations, that compact disconnected regions to achieve more efficient nested data parallelism. Experiments with a montane island ecosystem in southeast Arizona use Landsat TM data to characterize the processing framework, the spatial transformations, and the feature extraction algorithms.