Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing
Measuring the distribution of atmospheric aerosol concentration is of great significance for accurately assessing the scale of explosions and fires, the evolution of atmospheric environment, and so on. Due to the effect of atmospheric diffusion and other complex meteorological conditions, the distri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10233009/ |
| _version_ | 1797680929152434176 |
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| author | Weiyi Wang Dongsheng Yu Haibo Yu Minghan Yang Chidong Xu Xiaodong Fang |
| author_facet | Weiyi Wang Dongsheng Yu Haibo Yu Minghan Yang Chidong Xu Xiaodong Fang |
| author_sort | Weiyi Wang |
| collection | DOAJ |
| description | Measuring the distribution of atmospheric aerosol concentration is of great significance for accurately assessing the scale of explosions and fires, the evolution of atmospheric environment, and so on. Due to the effect of atmospheric diffusion and other complex meteorological conditions, the distribution of aerosol concentration will change significantly in a short period of time, which puts higher requirements on the speed of aerosol concentration measurement. The existing measurement technology mainly uses LIDAR for intensive sampling of aerosol in a region. Although lidar can achieve accurate measurement of the average concentration of atmospheric aerosol more conveniently, due to longer data processing time and more measurement sampling times, the timeliness of lidar remote sensing has decreased, resulting in the problem of difficult to capture the shape of atmospheric smoke and clouds in a timely manner. Therefore, this study proposes a fast reconstruction deep network model of aerosol extinction coefficient for lidar remote sensing from the perspective of sparse sampling-reconstruction. This model eliminates the feature distribution difference between lidar return signals under sparse sampling and conventional dense sampling by using unsupervised generative adversarial networks from the perspective of transfer learning. Then, the extinction coefficient reconstruction network with the encoding-decoding structure maps the low-dimensional abstract features of aerosol concentration distribution back to the high-dimensional extinction coefficient representation space. This model greatly reduces the sampling number of lidar, thereby reducing the total time required for aerosol extinction coefficient inversion. Experimental measurements of smoke and clouds above a thermal power plant show that the proposed deep network model can reduce more than 50% of the lidar sampling times within the allowable error range. This indicates that the model has the ability to significantly improve the remote sensing measurement efficiency of lidar for atmospheric aerosols. |
| first_indexed | 2024-03-11T23:37:24Z |
| format | Article |
| id | doaj.art-e51f462e58e34a0eb64b7530cc22162e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:37:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e51f462e58e34a0eb64b7530cc22162e2023-09-19T23:01:33ZengIEEEIEEE Access2169-35362023-01-0111988459885310.1109/ACCESS.2023.330955310233009Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote SensingWeiyi Wang0Dongsheng Yu1Haibo Yu2https://orcid.org/0000-0003-1290-9222Minghan Yang3https://orcid.org/0000-0002-6501-7132Chidong Xu4Xiaodong Fang5Science Island Branch, Graduate School, University of Science and Technology of China, Hefei, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, ChinaScience Island Branch, Graduate School, University of Science and Technology of China, Hefei, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, ChinaMeasuring the distribution of atmospheric aerosol concentration is of great significance for accurately assessing the scale of explosions and fires, the evolution of atmospheric environment, and so on. Due to the effect of atmospheric diffusion and other complex meteorological conditions, the distribution of aerosol concentration will change significantly in a short period of time, which puts higher requirements on the speed of aerosol concentration measurement. The existing measurement technology mainly uses LIDAR for intensive sampling of aerosol in a region. Although lidar can achieve accurate measurement of the average concentration of atmospheric aerosol more conveniently, due to longer data processing time and more measurement sampling times, the timeliness of lidar remote sensing has decreased, resulting in the problem of difficult to capture the shape of atmospheric smoke and clouds in a timely manner. Therefore, this study proposes a fast reconstruction deep network model of aerosol extinction coefficient for lidar remote sensing from the perspective of sparse sampling-reconstruction. This model eliminates the feature distribution difference between lidar return signals under sparse sampling and conventional dense sampling by using unsupervised generative adversarial networks from the perspective of transfer learning. Then, the extinction coefficient reconstruction network with the encoding-decoding structure maps the low-dimensional abstract features of aerosol concentration distribution back to the high-dimensional extinction coefficient representation space. This model greatly reduces the sampling number of lidar, thereby reducing the total time required for aerosol extinction coefficient inversion. Experimental measurements of smoke and clouds above a thermal power plant show that the proposed deep network model can reduce more than 50% of the lidar sampling times within the allowable error range. This indicates that the model has the ability to significantly improve the remote sensing measurement efficiency of lidar for atmospheric aerosols.https://ieeexplore.ieee.org/document/10233009/LIDARaerosol extinction coefficientdeep learninginverse problemfast reconstruction |
| spellingShingle | Weiyi Wang Dongsheng Yu Haibo Yu Minghan Yang Chidong Xu Xiaodong Fang Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing IEEE Access LIDAR aerosol extinction coefficient deep learning inverse problem fast reconstruction |
| title | Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing |
| title_full | Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing |
| title_fullStr | Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing |
| title_full_unstemmed | Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing |
| title_short | Sparse Inversion of Aerosol Concentration Distribution Based on LIDAR Remote Sensing |
| title_sort | sparse inversion of aerosol concentration distribution based on lidar remote sensing |
| topic | LIDAR aerosol extinction coefficient deep learning inverse problem fast reconstruction |
| url | https://ieeexplore.ieee.org/document/10233009/ |
| work_keys_str_mv | AT weiyiwang sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing AT dongshengyu sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing AT haiboyu sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing AT minghanyang sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing AT chidongxu sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing AT xiaodongfang sparseinversionofaerosolconcentrationdistributionbasedonlidarremotesensing |