A Transfer Learning Based Deep Learning Method for Mesoscale Convective Cloud Segmentation
Keywords:
Deep learning, Kalpana-1 infrared images, Mesoscale Convective Systems, Transfer learningAbstract
Risky weather events associated with Mesoscale Convective Systems (MCS) may end in considerable financial losses and occasionally even fatalities. Owing to the unpredictability of climate scenarios, little is known about the dynamics behind the development and deepening of MCS. Satellite images of MCS Clouds (MCSC) reveal a range of topologies, from open to closed, yet study on MCSC activities remains severely constrained. Through the use of high-resolution mathematical models of the atmosphere and the analysis of remote sensing imagery, high cloud-top temperature gradients, specific spatial shapes of temperature patterns, and other aspects of MCSCs can be investigated. In this study, deep learning (DL) methods are used to segment MCSC images using a transfer learning (TL) strategy. VGG-16 has been improved in the present work by fusing encoder-decoder architecture and taking cues from UNet architecture. The design that is generated is called ENDE-VGG. The proposed approach enables the collection of more pertinent data over a wider region. Images based on the Brightness Temperature in the infrared (K1-IR) channel by the Indian geostationary satellite Kalpana-1 are used in the present research. The TL technique produced a Dice coefficient of 0.935 on the validation data set, an intersection of union (IoU) of 0.875 on the K1-IR data set, and a mean IoU of 0.93. Additionally, using the test data, it obtained a 0.933 Dice coefficient and 0.875 IoU. ENDE-VGG performed better than the most sophisticated cloud image segmentation methods when using IoU as the loss function, based on numerous research studies.
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