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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. Due to the characteristics of rapid changes in aerosol distribution, it is difficult for this method to accurately reflect the overall aerosol distribution at a certain time. 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.

Sparse Inversion of Aerosol Concentration Distribution based on LIDAR Remote Sensing