2018 Paper on “A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images”

9 Jan 2020 2:54 PM

Abstract

Existing techniques for satellite-based tropical cyclone (TC) intensity estimation involve an explicit feature extraction step to model TC intensity on a set of relevant TC features or patterns such as eye formation and cloud organization. However, crafting such a feature set is often time-consuming and requires expert knowledge. In this paper, a convolutional neural network (CNN) approach, which eliminates explicit feature extraction, for estimating the intensity of tropical cyclones is proposed. Utilizing a Visual Geometry Group 19-1ayer CNN (VGG19) model pre-trained on ImageNet, transfer learning experiments were performed using grayscale IR images of TCs obtained from various geostationary satellites in the Western North Pacific region (1996 – 2016) to estimate TC intensity. The model re-trained on TC images achieved a root-mean-square error (RMSE) of 13.23 knots – a performance comparable to existing feature-based approaches (RMSE ranging from 12 to 20 knots). Moreover, the model was able to learn generic TC features that were previously identified in feature-based approaches as important indicators of TC intensity.

Authors

 


 


 

Jeffrey A. Aborot
Senior Science Research Specialist

Mr. Aborot is currently a researcher at DOST-ASTI’s Computer Software Division (CSD). As a researcher at CSD, he does applicative research on Artificial Intelligence in relation to satellite-captured images, legal texts, plants, and computing facility performance. He also supervises software engineering projects that has requirement for data analytics component where he oversees a software engineering team and programs core components of the software.

Mr. Aborot is a Bachelor’s degree graduate of the University of the Philippines Baguio. He is also currently a graduate student at the Department of Computer Science of the University of the Philippines Diliman. He is taking up a Master of Computer Science degree and is doing his research under the Algorithms and Complexity Laboratory. His research interest is Quantum Computing.

Jay Samuel L. Combinido
Senior Science Research Specialist

John Robert T. Mendoza
Senior Science Research Specialist

Published on

29 November 2018

Presented in

2018 24th International Conference on Pattern Recognition (ICPR)

Published at

IEEE Xplore Digital Library

Link to Publication

https://ieeexplore.ieee.org/document/8545593