基于主成分分析的空间外差干涉数据校正研究

Research on the Correction of Spatial Heterodyne Interference Data Based on Principal Component Analysis

空间外差光谱技术(SHS)是一种新型的高光谱遥感探测技术,被广泛应用于大气观测、天文遥感、物质识别等领域。通过空间外差光谱仪获取的二维实测干涉数据会受到多种影响因素干扰,其中高频噪声、不规则暗斑、干涉图非均匀性是其中最常见的影响。这些影响会降低复原光谱的精度,因此需要对这些影响发展有效的数据校正方法,提高反演光谱准确度。采用钾灯和氙灯两种光源产生准单色和连续光信号,以它们形成的干涉数据作为研究对象。针对这两种实测干涉图中的多种噪声影响,提出了一种基于主成分分析的空间外差干涉数据校正方法。首先采用一阶差分法对实测干涉图的所有行数据进行预处理,去除基线影响,将处理后的行数据进行傅里叶变换获得光谱数据。然后对所有行光谱数据进行主成分分析,计算出多个相互正交的主成分及每个主成分的贡献率,将贡献率小于2%的主成分当作噪声并加以扣除,保留其他主成分作为有效光谱信号进行光谱重建,重建光谱经过傅里叶逆变换得到校正后的干涉图。最后从干涉图和光谱两个维度对校正方法的有效性进行对比分析。结果表明,单色、连续两种光源实测干涉图中的暗斑得到有效扣除,非均匀性影响得到极大改善。针对暗斑影响明显的干涉图第536、600、982行数据,对比其光谱校正前后效果,结果显示:校正方法有效抑制了光谱中的高频噪声,使光谱变得平滑、清楚,特征峰等细节获得凸显,信噪比得到改善,三行光谱的均方误差分别由校正前的0.03777、0.02733、0.03099降低到校正后的0.01331、0.01220、0.01234,定量说明了方法的有效性。

Spatial Heterodyne Spectroscopy(SHS)is a new hyperspectral remote sensing detection technology widely used in atmospheric observation,astronomical remote sensing,material identification,and other fields.Two-dimensional measured interferometric data acquired by SHS can be interfered with by various influences,of which high-frequency noise,irregular dark spots,and interferogram nonuniformity are among the most common.These effects reduce the accuracy of the recovered spectra,and therefore,effective data correction methods need to be developed for these effects to improve the accuracy of the inverted spectra.In this paper,two light sources,potassium and xenon lamps,are used to generate quasi-monochromatic and continuous light signals,and the interference data formed by them are used as the object of study.A spatial heterodyne interferogram data correction method based on principal component analysis is proposed to address the effects of multiple noises in these two measured interferograms.Firstly,the first-order difference method is used to preprocess all the row data of the measured interferograms to remove the baseline effects,and Fourier transforms the processed row data to obtain the spectral data.Then,all the line spectral data are subjected to principal component analysis,multiple mutually orthogonal principal components and the contribution of each principal component is calculated,and the principal components with a contribution of less than 2%are treated as noise and deducted.In contrast,the other principal components are retained as valid spectral signals for spectral reconstruction,and the reconstructed spectra are inverse Fourier transformed to obtain a corrected interferogram.Finally,the effectiveness of the calibration methods is comparatively analyzed in terms of interferogram and spectral dimensions.The results show that the dark spots in the measured interferograms of monochromatic and continuous two light sources are effectively deducted,and the effect of non-uniformity is greatly improved.The effects before and after spectral correction are compared for the data in rows 536,600,and 982 of the interferogram,which are affected by the dark spots.The results show that the correction method effectively suppresses the high-frequency noise in the spectra and makes the spectra smooth and clear,and the details of the characteristic peaks and so on are highlighted.The signal-to-noise ratio is improved,and the mean square error of the three rows of spectra decreased from 0.03777,0.02733,and 0.03099 before correction to 0.01331,0.01220,and 0.01234 after correction,respectively,which quantitatively illustrates the effectiveness of the method.