Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Cao, Jun; Yuan, Yan; Su, Lijuan; Zhu, Conghui; Yan, Qiangqiang

doi:10.3390/s20041195

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…By altering the optical path difference (OPD) of two beams of light, we will acquire a set of interference fringes [12]. To obtain the spectral information, the spectrum reconstruction (SpecR) is used to transform interference fringes into spectral information, and the SpecR algorithm determines the quality and the further application of the spectral data [13,14]. The fast Fourier transform (FFT) plays a vital role in the SpecR of FTISs, which correlates spectral profiles with interference fringes [15].…”

Section: Introductionmentioning

confidence: 99%

Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers

Chen

et al. 2022

Remote Sensing

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Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).

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Section: Introductionmentioning

confidence: 99%

Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers

Chen

et al. 2022

Remote Sensing

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Reconstruction of Hadamard coded spectral data based on diffraction theory

刘文龙¹,

刘学斌²,

王爽³

et al. 2022

Acta Phys. Sin.

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Hardmard Transfer Imaging Spectrometer (HTIS) is a novel computationally optical system. Its characteristic of multi-channel multiplexing increases the luminous flux of the optical system without sacrificing spatial resolution, enabling the system's signal-to-noise ratio(SNR) to be significantly higher than traditional spectrometer’s. Encoding with digital mirror devices(DMD) in the system causes a serious diffraction effect that causes apparent degradation of imaging formation. For improving image quality and spectral accuracy of the reconstructed data cube, the Hadamard coded spectral imaging data degradation model is established based on the scalar diffraction theory. A data reconstruction algorithm is proposed based on Lucy Richard(L-R) algorithm. Through the simulation experiment, the process of image degradation is revealed. On the one hand, it proves that the degradation of system imaging diffraction is the main reason for the distortion of reconstructed data. On the other hand, it verifies the effectiveness of the correction method adopted in this paper. The evaluation result of the spectral angle distance of the restored data cube after LR correction is 0.1296, and the image similarity evaluation factor is better than 0.85. Compared with the reconstructed data before correction, the quality is greatly improved. The experimental results show that the algorithm has a good correction effect on the data cube reconstruction of HTIS.

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Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Cited by 2 publications

References 24 publications

Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers

Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers

Reconstruction of Hadamard coded spectral data based on diffraction theory

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