.png){kind=icon}
返回
Commodity cluster-based parallel processing of hyperspectral imagery
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2005.10.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA!s Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery. (C) 2005 Elsevier Inc. All rights reserved.
Keyword:
commodity cluster
hyperspectral imaging
load balance
mathematical morphology
parallelizable spatial/spectral partition (PSSP)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
暂无机构信息
引用论文
分享收藏Imaging spectroscopy and the Airborne Visible Infrared Imaging Spectrometer (AVIRIS)成像光谱与机载可见红外成像光谱仪 (AVIRIS)
PREAI
分享收藏A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏A formate bridged Ni(II) sheet and its 3D analogue in presence of a linear organic linker: Synthesis, crystal structure and magnetic propertiesPolyhedron
PolyhedronIF0
PREAI
分享收藏A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data
PREAI
分享收藏分享收藏
查看更多内容