Share Email Print

Proceedings Paper

Remote event detection and tracking using multiple heterogeneous satellite data fusion
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We describe an automated remote cyclone detection and tracking approach using heterogeneous data from multiple satellites. Single Earth orbiting satellite has been used in the past to detect and track events such as cyclones but suffer from major drawbacks due to limited spatio-temporal coverage. Our novel approach addresses the challenges in using heterogeneous data from multiple data sources for knowledge discovery, tracking and mining of cyclones. Moreover, it offers better detection performance and spatio-temporal resolutions. Our solution is sufficiently powerful that it generalizes to multiple sensor measurement modalities. Our approach consists of: (i) feature extraction from each sensor measurement, (ii) an ensemble classifier for cyclone detection, and (iii) knowledge sharing between the different remote sensor measurements. Our extensive experimental results demonstrate (i) the superior performance of our cyclone detector compared to previous work on preprocessed historical data, (ii) stable performance of our cyclone detector when it is applied on different geographical regions (Western Pacific Ocean and the North Atlantic Ocean), (iii) meaningful knowledge derived from the cyclone detector output, and (iv) the performance quality of our automated cyclone detection and tracking solution closely match the cyclone best track information from the National Hurricane Center.

Paper Details

Date Published: 13 April 2009
PDF: 12 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 73400D (13 April 2009); doi: 10.1117/12.820488
Show Author Affiliations
Ashit Talukder, Jet Propulsion Lab. (United States)
Shen-Shyang Ho, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)

© SPIE. Terms of Use
Back to Top