Object-oriented design for parallel processing of massive remote sensing data

Author

Li, Bing ; Tang, Feixiong ; Li, Tong ; Liu, Suhong ; Ye, Ming

Author_Institution

State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing, China

fYear

2012

fDate

19-20 May 2012

Firstpage

1205

Lastpage

1208

Abstract

When remote sensing data of earth observation reach the P level, distributed processing of global remote sensing data and improvement of digital image processing speed have become a research focus. In this article, a system of object-oriented metadata definition is developed for describing parameter inversion model and dependency of the remote sensing data. Based on the system, a space-time partitioning multi-granularity model is established and then applied to partitioning massive remote sensing data for parallel processing. Our results show that the model can provide data-based parallel processing and improve the capabilities of the massive remote sensing data processing.

Keywords

geophysical image processing; object-oriented methods; parallel processing; remote sensing; P level; digital image processing speed; distributed processing; earth observation; massive remote sensing data; object-oriented design; object-oriented meta-data definition; parallel processing; parameter inversion model; space-time partitioning multigranularity model; Data models; Data processing; Distributed databases; Load modeling; Message systems; Parallel processing; Remote sensing; massive remote sensing data; model; multi-granularity partitioning; parallel processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems and Informatics (ICSAI), 2012 International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4673-0198-5

Type

conf

DOI

10.1109/ICSAI.2012.6223251

Filename

6223251