Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of dat...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 27; no. 2; pp. 692 - 702
Main Authors Pradhan, Ritesh, Aygun, Ramazan S., Maskey, Manil, Ramachandran, Rahul, Cecil, Daniel J.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.02.2018
Subjects
Computer architecture
convolutional neural networks
Deep learning
Estimation
Feature extraction
Hurricanes
image processing
Neural networks
tropical cyclone category and intensity estimation
Tropical cyclones
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Summary:Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related to intensity estimation. In this study, we design a deep convolutional neural network architecture for categorizing hurricanes based on intensity using graphics processing unit. Our model has achieved better accuracy and lower root-mean-square error by just using satellite images than 'state-of-the-art' techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2766358