Research on Phase Correction of Spatial Heterodyne Interferogram Based on Recurrent Neural Network

The spatial heterodyne spectrometer, used in gas and mineral detection, requires phase error correction for data accuracy. Traditionally, this needs ground-based system calibration. In space, however, environmental changes can alter instrument parameters, making recalibration of the phase error surface difficult. Therefore, there is an urgent need to develop a novel phase correction method that does not require measuring the phase error surface. This study focuses on the phase errors in the interferogram and establishes a predictive model using a recurrent neural network by analyzing the relationship between spectra with errors and those without errors, thereby achieving correction of the error-containing spectra. The results indicate that, in the absence of known phase errors, the recurrent neural network can effectively perform phase error correction, yielding corrected spectra that align closely with the profiles of error-free spectra, while significantly reducing residuals and standard deviations. Compared to convolutional methods, the recurrent neural network approach demonstrates superior correction efficacy and good applicability. Therefore, the recurrent neural network method can be effectively applied to phase correction for various types of spatial heterodyne interferograms.