基于神经网络的非线性大气修正实现红外目标辐射测量

红外辐射测量技术是表征目标红外特征的重要手段，而大气修正是获得目标真实辐射的必要步骤。提出了一种提高远距离目标红外辐射测量精度的非线性大气修正（NLAC）方法。该方法利用近距离标准参考源测量（NRSRM）来计算实时环境中不同位置的实际大气透过率和程辐射。相应条件下的理论大气透过率和程辐射也可以从大气辐射传输软件中获得。应用神经网络技术对两者之间的非线性关系进行拟合。因此，可以预测远距离的大气透过率和程辐射，以实现大气修正。为了进行比较，还进行了简单的线性大气修正（LAC）与线性增强大气修正（LEAC）。实验结果表明，该方法的红外辐射测量平均误差为6.45%，远低于常规方法，线性大气修正方法和线性增强大气修正，分别为16.17%，11.27%和7.44%。

Nonlinear atmospheric correction based on neural network for infrared target radiometry

Infrared radiometry technology is an important means to characterize the infrared signature of targets, and atmospheric correction is a requisite step to obtain the real radiance of targets. A nonlinear atmospheric correction (NLAC) method was proposed to improve the infrared radiometric accuracy for long distance targets in this paper. This method used near-range standard reference source measurement (NRSRM) to calculate the actual atmospheric transmittance and path radiation simultaneously at different locations in a real-time environment. And the theoretical atmospheric transmission and path radiation under the corresponding conditions could be obtained from the atmospheric radiation transmission software as well. Neural network technology was applied to fit the non-linear relationship between them. Thus, the atmospheric transmittance and path radiation over long distances could be predicted to achieve atmospheric correction. Simpler linear atmospheric correction (LAC) and linear enhancement atmospheric correction (LEAC) were also carried out for comparison. The experimental results indicate that the infrared radiometric average error of the proposed method is 6.45%, which is much lower than that of the conventional method, LAC and LEAC that are 16.17%, 11.27% and 7.44%, respectively.