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Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing. / Lin, Huichao; Xu, Xiaolong; Bilal, Muhammad et al.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 16, 19.07.2023, p. 6948-6957.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lin, H, Xu, X, Bilal, M, Cheng, Y & Liu, D 2023, 'Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 6948-6957. https://doi.org/10.1109/JSTARS.2023.3296908

APA

Lin, H., Xu, X., Bilal, M., Cheng, Y., & Liu, D. (2023). Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 6948-6957. https://doi.org/10.1109/JSTARS.2023.3296908

Vancouver

Lin H, Xu X, Bilal M, Cheng Y, Liu D. Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023 Jul 19;16:6948-6957. doi: 10.1109/JSTARS.2023.3296908

Author

Lin, Huichao ; Xu, Xiaolong ; Bilal, Muhammad et al. / Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023 ; Vol. 16. pp. 6948-6957.

Bibtex

@article{894c77f0a0764b4a8356425af635946e,
title = "Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing",
abstract = "Meteorological radar data are essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multichannel meteorological remote sensing satellite data and removing the interference caused by nonprecipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this article, we propose a novel method that combines the advanced encryption standard method to protect the transmission of remote sensing data, a multiscale feature fusion module to extract multiscale features from multichannel meteorological remote sensing satellite data, and an attention technique to reduce the interference of nonprecipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55, and 0.99 on probability of detection, false alarm ratio, critical success index, Heidke skill score, and accuracy scores, respectively.",
keywords = "Cryptography, deep learning, Hamawari-8, radar reflectivity factor (RF), remote sensing",
author = "Huichao Lin and Xiaolong Xu and Muhammad Bilal and Yong Cheng and Dongqing Liu",
year = "2023",
month = jul,
day = "19",
doi = "10.1109/JSTARS.2023.3296908",
language = "English",
volume = "16",
pages = "6948--6957",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing

AU - Lin, Huichao

AU - Xu, Xiaolong

AU - Bilal, Muhammad

AU - Cheng, Yong

AU - Liu, Dongqing

PY - 2023/7/19

Y1 - 2023/7/19

N2 - Meteorological radar data are essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multichannel meteorological remote sensing satellite data and removing the interference caused by nonprecipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this article, we propose a novel method that combines the advanced encryption standard method to protect the transmission of remote sensing data, a multiscale feature fusion module to extract multiscale features from multichannel meteorological remote sensing satellite data, and an attention technique to reduce the interference of nonprecipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55, and 0.99 on probability of detection, false alarm ratio, critical success index, Heidke skill score, and accuracy scores, respectively.

AB - Meteorological radar data are essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multichannel meteorological remote sensing satellite data and removing the interference caused by nonprecipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this article, we propose a novel method that combines the advanced encryption standard method to protect the transmission of remote sensing data, a multiscale feature fusion module to extract multiscale features from multichannel meteorological remote sensing satellite data, and an attention technique to reduce the interference of nonprecipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55, and 0.99 on probability of detection, false alarm ratio, critical success index, Heidke skill score, and accuracy scores, respectively.

KW - Cryptography

KW - deep learning

KW - Hamawari-8

KW - radar reflectivity factor (RF)

KW - remote sensing

U2 - 10.1109/JSTARS.2023.3296908

DO - 10.1109/JSTARS.2023.3296908

M3 - Journal article

AN - SCOPUS:85165243915

VL - 16

SP - 6948

EP - 6957

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

ER -