大气校正是遥感定量化的一个重要环节,其常用方法是暗目标法。该方法适用于浓密植被地区,但在植被覆盖度较低的地区,暗目标法的适用性较差。提出了一种基于光谱匹配的大气校正方法,以城市地区的不变目标为切入点,针对高分一号(GF-1)卫星全色和多光谱(PMS2)传感器相机开展大气校正方法的研究。该方法利用6S辐射传输模型构建大气校正参数查找表,得到了影像上的水泥路面在不同大气条件下的反演光谱;同时,利用水泥路面的平均实测光谱作为参考光谱,通过参考光谱与测试光谱的光谱角度匹配,找到最相近的光谱曲线,用以确定大气校正参数,并对影像进行大气校正。实验结果表明:该大气校正方法效果良好,反演得到的地表反射率与典型地物的光谱数据比较吻合,更好地还原了地表的真实情况,为植被稀疏地区的大气校正工作提供了新思路。

Atmospheric correction is an important part of remote sensing quantification. The commonly used atmospheric correction method is the dark target method, which is suitable for application in dense vegetation areas but is less suitable in areas with low coverage of vegetation. In this study, an atmospheric correction method is proposed based on spectral matching. Herein, an invariable target in urban areas is used as the entry point. Further, we develop an atmospheric correction method for the Gao Fen-1 (GF-1) satellite panchromatic and multispectral sensor (PMS) camera. This method uses the 6S radiation transmission model to construct an atmospheric correction parameter lookup table for obtaining the inversion spectra of cement pavements from different images under different atmospheric conditions, and the average measured spectrum of the cement pavement is considered to be the reference spectrum. By the angle matching between the test spectrum and the reference spectrum, the closest spectral curve is estimated, the atmospheric correction parameters are determined, and the image is atmospherically corrected. The experimental results denote that this method works well, and the surface reflectivity obtained based on the inversion is consistent with the typical ground spectral data, which restores the surface to its actual situation and provides a novel atmospheric correction method that can be applied in areas having sparse vegetation.