A distributed spectral-screening PCT algorithm

Authors:
Tiranee Achalakul;Stephen Taylor
Affiliations:
Department of Computer Engineering, King Mongkut's University of Technology Thonburi, 91 suksawad 48, Tung-kru, Bangkok 10140, Thailand;Thayer School of Engineering, Dartmouth College, 8000 Cummings, Hanover, NH
Venue:
Journal of Parallel and Distributed Computing
Year:
2003

Citing 11
Cited 8

Introduction to algorithms

Introduction to algorithms
An introduction to parallel programming

An introduction to parallel programming
Principal Components Analysis (PCA)

Computers & Geosciences
Multisensor image fusion using the wavelet transform

Graphical Models and Image Processing
Independent component analysis: theory and applications

Independent component analysis: theory and applications
Automatic Performance Prediction of Parallel Programs

Automatic Performance Prediction of Parallel Programs
Remote Sensing Digital Image Analysis: An Introduction

Remote Sensing Digital Image Analysis: An Introduction
Mathematical Techniques in Multisensor Data Fusion

Mathematical Techniques in Multisensor Data Fusion
Digital Image Processing

Digital Image Processing
Ray Casting on Shared-Memory Architectures: Memory-Hierarchy Considerations in Volume Rendering

IEEE Concurrency
Performance modeling for concurrent particle simulations

Performance modeling for concurrent particle simulations

Commodity cluster-based parallel processing of hyperspectral imagery

Journal of Parallel and Distributed Computing
Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations

Journal of Parallel and Distributed Computing
An experimental comparison of parallel algorithms for hyperspectral analysis using heterogeneous and homogeneous networks of workstations

Parallel Computing
Efficient Collective Communication Paradigms for Hyperspectral Imaging Algorithms Using HeteroMPI

Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Parallel Processing of Remotely Sensed Hyperspectral Images On Heterogeneous Networks of Workstations Using HeteroMPI

International Journal of High Performance Computing Applications
Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery

International Journal of High Performance Computing Applications
Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems

Journal of Signal Processing Systems
High-performance computing in remotely sensed hyperspectral imaging: the pixel purity index algorithm as a case study

IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a novel distributed algorithm for use in remote-sensing, medical image analysis, and surveillance applications. The algorithm combines spectral-screening classification with the principal component transform, and human-centered mapping. It fuses a multi- or hyper-spectral image set into a single color-composite image that maximizes the impact of spectral variation on the human visual system. The algorithm operates on distributed collections of shared-memory multiprocessors that are connected through high-performance networking. Scenes taken from a standard 210 frame remote-sensing data set, collected with the hyper-spectral digital imagery collection experiment airborne imaging spectrometer, are used to assess the algorithms image quality, performance, and scaling. The algorithm is supported with a predictive analytical model that allows its performance to be assessed for a wide variety of typical variations in use. For example, changes to the number of spectra, image resolution, processor speed, memory size, network bandwidth/latency, and granularity of decomposition. The motivation in building a performance model is to assess the impact of changes in technology and problem size associated with different applications, allowing cost-performance tradeoffs to be assessed.