Feature based visualization of geophysical data

Author

Yang, Qing ; Arvin, Bahram P.

Author_Institution

Dept. of Comput. Sci., Lawrence Berkeley Nat. Lab., CA, USA

Volume

2

fYear

2000

fDate

2000

Firstpage

276

Abstract

Our goal is to develop a feature based framework for data mining and forecasting from geophysical data fields. These data may be generated from either numerical simulation models or space based platforms. This paper focuses on pertinent features from sea surface temperature (SST) fields that are observed with the AVHRR satellite. Our contribution consist of three components: (1) A method for tracking feature velocities from from fluid motion with incompressibility constraint, (2) a method for localizing singular events such as vortices and saddle points from underlying feature velocities, and (3) application of our protocol to 12 years of high resolution real data to reveal novel seasonal and inter-annual trends based on computed events

Keywords

data mining; data visualisation; geophysical signal processing; geophysics computing; data mining; feature velocities; geophysical data; incompressibility constraint; sea surface temperature; visualization; Data visualization; Geophysics computing; Numerical models; Numerical simulation; Ocean temperature; Satellites; Sea surface; Spatial resolution; Temperature sensors; Tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on

Conference_Location

Hilton Head Island, SC

ISSN

1063-6919

Print_ISBN

0-7695-0662-3

Type

conf

DOI

10.1109/CVPR.2000.854807

Filename

854807