A distributed spectral-screening PCT algorithm

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...

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Vydáno v:Journal of parallel and distributed computing Ročník 63; číslo 3; s. 373 - 384
Hlavní autoři: Achalakul, Tiranee, Taylor, Stephen
Médium: Journal Article
Jazyk:angličtina
Vydáno: San Diego, CA Elsevier Inc 01.03.2003
Elsevier
Témata:
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Distributed algorithm
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Performance prediction
Principal component transform
Spectral angle classification
Performance prediction
Principal component transform
Spectral angle classification
Distributed algorithm
Hyperspectral imaging sensor
Screening
Prediction
Spectral classification
Principal component analysis
ISSN:0743-7315, 1096-0848
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Shrnutí: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.
ISSN:0743-7315
1096-0848
DOI:10.1016/S0743-7315(03)00017-0