光学 精密工程, 2014, 22 (11): 2914, 网络出版: 2014-12-08   

基于支持向量机预测偏振模式

Prediction of polarization pattern by SVM
作者单位
1 中国科学院 安徽光学精密机械研究所 光学遥感中心, 安徽 合肥 230031
2 安徽建筑大学 机械与电气工程学院, 安徽 合肥 230601
摘要
基于多光谱多角度偏振辐射探测研究了大气偏振模式预测方法。首先, 介绍了航空多角度偏振辐射计的探测原理与支持向量机回归算法;然后, 从矢量传输模型出发, 说明大气状况不变时, 偏振模式主要取决于地表特征与观测几何, 并介绍了观测几何与姿态之间的关系以及地表特征的表达形式;最后, 在考虑平台姿态与地表特征的情况下, 利用支持向量机回归算法预测了航空多角度偏振辐射计的探测偏振度, 并对预测与实际试验探测的偏振度进行了比较。结果显示: 偏振度预测误差小于1%, 影响模型精度的主要因素不是姿态变化本身, 而是姿态改变造成的观测地表特性变化。
Abstract
On the basis of multi-spectral multi-angular radiative polarization measurements, a prediction method for atmospheric polarization patterns is investigated. Firstly, the principle of an Aviation Multi-angle Polarimetric Radiometer (AMPR) and the regression algorithm of a Support Vector Machine (SVM) are introduced. Then, according to Vector Radiative Transfer Equation (VRTE), it points out that the atmospheric polarization pattern is primarily dependent on view geometry and surface features when atmosphere condition is invariant. Meanwhile, it introduces the relationship between view geometry and platform attitude and the expressing form of surface features. Finally, how to use the regression algorithm of SVM to predict the detected polarization degree of the APMR and to validate the application process is introduced in the consideration of the surface features and platform attitudes. Furthermore, the predicted degrees of polarization is also compared to that of real measurements. The results indicate that the error of polarization degree predicted is less than 1%. It concludes that the serious factor to affect the model accuracy is not the change of attitude but the variation of underlying surface property caused by the changed attitudes.

汪方斌, 洪津, 孙晓兵, 王羿, 胡亚东. 基于支持向量机预测偏振模式[J]. 光学 精密工程, 2014, 22(11): 2914. WANG Fang-bin, HONG Jin, SUN Xiao-bing, WANG Yi, HU Ya-dong. Prediction of polarization pattern by SVM[J]. Optics and Precision Engineering, 2014, 22(11): 2914.

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