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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery

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Title
Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery
Author
Lee, JuhyunKim, MiaeIm, JunghoHan, HyangsunHan, Deahyeon
Issue Date
2021-08
Publisher
TAYLOR & FRANCIS LTD
Citation
GISCIENCE & REMOTE SENSING
Abstract
Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).
URI
https://scholarworks.unist.ac.kr/handle/201301/53544
URL
https://www.tandfonline.com/doi/full/10.1080/15481603.2021.1960075
DOI
10.1080/15481603.2021.1960075
ISSN
1548-1603
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