基于大气污染物二维分布遥测系统,开展了被动成像差分吸收光谱技术和图像优化算法相结合的大气污染物二维分布遥测研究。将紫外光谱和可视化图像的双通道系统相结合,实现了污染物浓度信息与可视化图像的精确匹配。针对复杂背景下污染物的数据处理问题,尤其是受浓烟、障碍物遮挡等情况下的数据缺失问题,对观测值进行筛选和处理,再结合烟羽扩散模型和曲面三次样条插值算法对整个区域内的浓度信息进行优化,从而快速获得高空间分辨的污染物二维分布图像信息。

Based on the two-dimensional distribution remote measurement system of air pollutants, this study examined the two-dimensional distribution remote measurement of pollutants by the passive imaging differential optical absorption spectroscopy technology combined with the image optimization algorithms. The concentration information of pollutants and the visual images were accurately matched using a dual-channel system that combines the ultraviolet spectra with the visual images. For the problems of the data processing of pollutants under complex backgrounds, particularly the problem that of data missing under conditions of dense smoke and obstacles, the observed values were screened and processed. Subsequently, the smoke plume diffusion model was combined with the curved cubic spline interpolation algorithm to optimize the concentration information in the entire area, and thus to quickly obtain the two-dimensional distribution image information of pollutants with high spatial resolution.