Feature based visualization of geophysical data

被引：0
|
作者：
Yang, Q [1 ]
Parvin, B [1 ]
机构：
[1] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
来源：
IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, VOL II | 2000年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：276 / 281
页数：6
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