A Deep Learning Framework for the Detection of Tropical Cyclones From Satellite Images
Autor: | Sayali Kulkarni, Kshitij Alwadhi, Sandeep Sukumaran, Navya Jain, Viju John, K. S. S. Sai Srujan, Aravind Nair, Hariprasad Kodamana |
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Rok vydání: | 2022 |
Předmět: |
Artificial neural network
business.industry Computer science Deep learning Bayesian optimization Filter (signal processing) Geotechnical Engineering and Engineering Geology Wind speed Classifier (linguistics) Satellite Artificial intelligence Tropical cyclone Electrical and Electronic Engineering business Remote sensing |
Zdroj: | IEEE Geoscience and Remote Sensing Letters. 19:1-5 |
ISSN: | 1558-0571 1545-598X |
DOI: | 10.1109/lgrs.2021.3131638 |
Popis: | Tropical cyclones (TCs) are the most destructive weather systems that form over the tropical oceans, with 90 storms forming globally every year. The timely detection and tracking of TCs are important for advanced warning to the affected regions. As these storms form over the open oceans far from the continents, remote sensing plays a crucial role in detecting them. Here we present an automated TC detection from satellite images based on a novel deep learning technique. In this study, we propose a multi-staged deep learning framework for the detection of TCs, including, (i) a detector - Mask Region-Convolutional Neural Network (R-CNN), (ii) a wind speed filter, and (iii) a classifier - CNN. The hyperparameters of the entire pipeline is optimized to showcase the best performance using Bayesian optimization. Results indicate that the proposed approach yields high precision (97.10%), specificity (97.59%), and accuracy (86.55%) for test images. |
Databáze: | OpenAIRE |
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