Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing
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. Meteo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10187124/ |
| _version_ | 1797755857022222336 |
|---|---|
| author | Huichao Lin Xiaolong Xu Muhammad Bilal Yong Cheng Dongqing Liu |
| author_facet | Huichao Lin Xiaolong Xu Muhammad Bilal Yong Cheng Dongqing Liu |
| author_sort | Huichao Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T17:53:10Z |
| format | Article |
| id | doaj.art-03aa937d250748868123741274798894 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-12T17:53:10Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-03aa937d2507488681237412747988942023-08-02T23:00:06ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01166948695710.1109/JSTARS.2023.329690810187124Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote SensingHuichao Lin0https://orcid.org/0009-0000-1948-7013Xiaolong Xu1https://orcid.org/0000-0003-4879-9803Muhammad Bilal2https://orcid.org/0000-0003-4221-0877Yong Cheng3https://orcid.org/0009-0001-2287-2809Dongqing Liu4https://orcid.org/0000-0003-1258-4499School of Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Software, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin, South KoreaSchool of Software, Nanjing University of Information Science and Technology, Nanjing, ChinaNanjing Meteorological Bureau, Nanjing, ChinaMeteorological 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.https://ieeexplore.ieee.org/document/10187124/Cryptographydeep learningHamawari-8radar reflectivity factor (RF)remote sensing |
| spellingShingle | Huichao Lin Xiaolong Xu Muhammad Bilal Yong Cheng Dongqing Liu 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 Cryptography deep learning Hamawari-8 radar reflectivity factor (RF) remote sensing |
| title | Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing |
| title_full | Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing |
| title_fullStr | Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing |
| title_full_unstemmed | Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing |
| title_short | Intelligent Retrieval of Radar Reflectivity Factor With Privacy Protection Under Meteorological Satellite Remote Sensing |
| title_sort | intelligent retrieval of radar reflectivity factor with privacy protection under meteorological satellite remote sensing |
| topic | Cryptography deep learning Hamawari-8 radar reflectivity factor (RF) remote sensing |
| url | https://ieeexplore.ieee.org/document/10187124/ |
| work_keys_str_mv | AT huichaolin intelligentretrievalofradarreflectivityfactorwithprivacyprotectionundermeteorologicalsatelliteremotesensing AT xiaolongxu intelligentretrievalofradarreflectivityfactorwithprivacyprotectionundermeteorologicalsatelliteremotesensing AT muhammadbilal intelligentretrievalofradarreflectivityfactorwithprivacyprotectionundermeteorologicalsatelliteremotesensing AT yongcheng intelligentretrievalofradarreflectivityfactorwithprivacyprotectionundermeteorologicalsatelliteremotesensing AT dongqingliu intelligentretrievalofradarreflectivityfactorwithprivacyprotectionundermeteorologicalsatelliteremotesensing |