Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

• Published in: IEEE 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
• Language: English
• 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
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2017.2766358

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.