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 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 interferojgrams to remove the baseline effects, and Fourier transforms the processed row data to obtain the spectral data. Then. all the line spectral data 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 highlighted. The signal-to-noise ratio is improved, and the mean square error of the three rows of spectra decreased from 0.037 77, 0.027 33, and 0.030 99 before correction to 0.013 31, 0.012 20, and 0.012 34 after correction, respectively, which quantitatively illustrates the effectiveness of the method.
