A low pressure system with winds rotating in an anticlockwise (clockwise) direction in the northern (southern) hemisphere with a minimum sustained wind speed of 34 knots (62kmph).
Published in Chapter:
A Robust Method for Classification and Localization of Satellite Cyclonic Images Over the Bay of Bengal and the Arabian Sea Using Deep Learning
Manikyala Rao Tankala (IMD, Ministry of Earth Sciences, India), Samuel Stella (IMD, Ministry of Earth Sciences, India), Prayek Sandepogu (IMD, Ministry of Earth Sciences, India), Kondaveeti Nanda Gopal (IMD, Ministry of Earth Sciences, India), Ramesh Babu Mamillapalli (IMD, Ministry of Earth Sciences, India), and Devarakonda Rambabu (IMD, Ministry of Earth Sciences, India)
Copyright: © 2022
|Pages: 17
DOI: 10.4018/978-1-6684-3981-4.ch014
Abstract
According to recent findings, deep learning algorithm outperforms in many tasks like image classification, image segmentation, image recognition, etc. in the field of computer vision. With the help of deep learning, classification tasks on remote sensing image data can attain better performance compared to traditional approaches. This chapter primarily demonstrates how residual neural networks are used to classify satellite images of cyclones in the Bay of Bengal (BoB) and the Arabian Sea (Arab Sea). The authors further discovered the cyclones' locations and investigated using satellite images in the infrared and visible bands of electromagnetic spectrum. From the evaluation metrics, the neural network looks to be capable of correctly identifying the cyclonic storm utilising Gradient Class Activation Mapping (Grad-CAM). Satellite images of both cyclone storm and non-cyclone storm are analysed for cyclonic storm recognition and classification.