Dual-Interference Channels Static Fourier Transform Imaging Spectrometer Based on Stepped Micro-Mirror: Data Processing and Experiment Research

The use of a dual-interference channels static Fourier transform imaging spectrometer based on stepped micro-mirror (D-SIFTS) for environmental gas monitoring has the advantages of high throughput, a compact structure, and a stable performance. It also has the characteristics of both a broad spectral range and high spectral resolution. However, its unique structural features also bring many problems for subsequent data processing, mainly including the complex distribution of the interference data, the low signal-to-noise ratio (SNR) of infrared scene images, and a unique inversion process of material information. To this end, this paper proposes a method of image and spectra information processing and gas concentration inversion. A multiscale enhancement algorithm for infrared images incorporating wavelet denoising is used to obtain high-quality remote sensing scene images, and spectral reconstruction optimization algorithms, such as interference intensity sequence resampling, are used to obtain accurate spectral information; the quantitative calibration model of the detected gas concentration is established to achieve high-precision inversion of gas concentration, and its distribution is visualized in combination with the scene image. Finally, the effectiveness and accuracy of the data processing algorithm are verified through the use of several experiments, which provide essential theoretical guidance and technical support for the practical applications of D-SIFTS.