Data correction techniques for the airborne large-aperture static image spectrometer based on image registration

Zhang, Geng; Shi, Dean; Wang, Shuang; Yu, Tao; Hu, Bingliang

doi:10.1117/1.jrs.9.095088

Cited by 5 publications

(8 citation statements)

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“…According to Equations (3) and (8), the OPD of the target is only related to the value of y p in the ideal case; when misregistration occurs, the value of x p also affects the OPD. Since tan 2 θ 1 in this case, the OPD deviation of P can be rewritten and simplified as:…”

Section: Modeling and Analysis Of The Registration Errormentioning

confidence: 99%

“…TSMFTISs were developed as airborne/spaceborne hyperspectral imagers for earth remote sensing because of its high throughput advantage. Over a long period, the TSMFTISs developed include the large aperture static imaging spectrometer (LASIS) [8,9], the high etendue imaging Fourier transform spectrometer (HEIFTS) [10,11], Caméra Hyperspectrale de Démonstration (CaHyD) [12], the aerospace leap-frog imaging static interferometer for Earth observation (ALISEO) [13,14], the medium-and long-wave infrared spectral imaging instrument SIELETERS [15,16], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the OPDs correspond to the interferogram changes, resulting in an inability to accurately recover the spectral information. Zhang et al [8] constructed a correction system with two theodolites, rotating table and parallel optical tubes, calibrated the detector registration error, calculated the centroid position of the star at the position of zero OPD of the spectral dimension, and obtained the registration error by fitting the slope of the line with the least-squares line. The calibration method has complex operation, high computational complexity, and no real-time ability.…”

Section: Introductionmentioning

confidence: 99%

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

Cao

Yuan

et al. 2020

Sensors

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Temporally-spatially modulated Fourier transform imaging spectrometers (TSMFTISs) provide high-throughout-type push-broom spectrometry with both temporal and spatial modulation features. The system requires strict registration between the detector and the interferogram. However, registration errors are unavoidable and directly change the corresponding optical path difference values of the interferogram. As a result, the interferogram should be corrected before restoring the spectrum. In order to obtain the correct optical path difference (OPD) values, an online registration error correction method based on robust least-square linear fitting is presented. The model of the registration error was constructed to analyze its effect on the reconstructed spectra. Fitting methods were used to obtain correct optical path difference information. Simulations based on the proposed method were performed to determine the influence of the registration error on the restored spectra and the effectiveness of the proposed correction method. The simulation results prove that the accuracy of the recovered spectrum can be improved after correcting the interferogram deviation caused by the registration error. The experimental data were also corrected using the proposed methods.

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Section: Modeling and Analysis Of The Registration Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cao

Yuan

et al. 2020

Sensors

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“…Imaging interferometers, also called Fourier transform imaging spectrometers (FTISs), have several superiorities, including high throughput, high wavenumber accuracy, high resolution, lower weight, and smaller size [1][2][3]. Due to these advantages, FTISs have been widely used in remote sensing of the moon, earth, and space [4,5]. With rapid developments in technology, various types of interferometric spectrometers that can obtain tens or even hundreds of hyperspectral data [6] have been established, such as the temporary modulated imaging interferometer proposed by Michelson, the spatial interferometric imaging spectrometer, and the spatially-temporally modulated interferometric imaging spectrometer [7].…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms all achieved good spectrum reconstruction in terms of resolution, however, they were not suited for polychromatic SpecR, particularly for low signal-to-noise ratio data. Hence, currently most of the methods for SpecR are still based on FFT [1,3,4].…”

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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Registration of ski resort point cloud based on improved algorithm of principal component analysis

Wang¹,

Zhao²,

Zhang³

2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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Data correction techniques for the airborne large-aperture static image spectrometer based on image registration

Cited by 5 publications

References 0 publications

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

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

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

Registration of ski resort point cloud based on improved algorithm of principal component analysis

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