大数据分析的复杂环境偏振成像图像优化研究

Polarization imaging image optimization in complex environment based on big data analysis

针对传统方法无法描述复杂环境偏振成像图像的变化特点,导致复杂环境偏振成像图像优化效果差的问题,提出了大数据分析的复杂环境偏振成像图像优化方法。综合考虑复杂环境偏振成像环境,匹配复杂环境差异尺度,利用最小二乘法整理匹配后形成的数值邻域,大数据分析过程确定复杂环境偏振成像图像的成像轮廓,采用数值点标定的处理过程,控制补偿的复杂环境偏振成像图像点,最终形成复杂环境偏振成像图像。与其它复杂环境偏振成像图像优化方法进行了对比实验,实验结果表明:设计方法的光线偏差数值最小,在10 cd左右,计算耗时为0.2 s,数据质量参数在0.94左右,均优于其他方法。

Traditional methods can’t describe the changing characteristics of polarization imaging images in complex environment, which leads to poor optimization effect of polarization imaging images in complex environment. To solve this problem, a polarization imaging image optimization method in complex environment based on big data analysis is proposed. Considering polarization imaging environment of complex environment comprehensively, matching the difference scale of complex environment, sorting out the numerical neighborhood formed after matching by least square method, determining the imaging contour of polarization imaging image of complex environment by big data analysis process, and controlling compensated polarization imaging image points of complex environment by numerical point calibration process, finally forming polarization imaging image of complex environment. Compared with other polarization imaging image optimization methods in complex environment, the experimental results show that the designed method has the smallest ray deviation value, which is about 10 cd, the calculation time is 0.2 s, and the data quality parameter is about 0.94, which is superior to other methods.