Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

Ritesh Pradhan

doi:10.1109/TIP.2017.2766358

Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

2018-02-01

172

PRE

AI

DOI：10.1109/TIP.2017.2766358

原文链接

原文求助

摘要

En 中文

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.

Keyword:

Deep learning

image processing

convolutional neural networks

tropical cyclone category and intensity estimation

Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

摘要

AI总结

期刊

机构