Paper
24 October 2017 Fast data reconstructed method of Fourier transform imaging spectrometer based on multi-core CPU
Chunchao Yu, Debiao Du, Zongze Xia, Li Song, Weijian Zheng, Min Yan, Zhenggang Lei
Author Affiliations +
Proceedings Volume 10461, AOPC 2017: Optical Spectroscopy and Imaging; 1046118 (2017) https://doi.org/10.1117/12.2285195
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Imaging spectrometer can gain two-dimensional space image and one-dimensional spectrum at the same time, which shows high utility in color and spectral measurements, the true color image synthesis, military reconnaissance and so on. In order to realize the fast reconstructed processing of the Fourier transform imaging spectrometer data, the paper designed the optimization reconstructed algorithm with OpenMP parallel calculating technology, which was further used for the optimization process for the HyperSpectral Imager of ‘HJ-1’ Chinese satellite. The results show that the method based on multi-core parallel computing technology can control the multi-core CPU hardware resources competently and significantly enhance the calculation of the spectrum reconstruction processing efficiency. If the technology is applied to more cores workstation in parallel computing, it will be possible to complete Fourier transform imaging spectrometer real-time data processing with a single computer.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chunchao Yu, Debiao Du, Zongze Xia, Li Song, Weijian Zheng, Min Yan, and Zhenggang Lei "Fast data reconstructed method of Fourier transform imaging spectrometer based on multi-core CPU", Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 1046118 (24 October 2017); https://doi.org/10.1117/12.2285195
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Fourier transforms

Spectroscopy

Parallel computing

Back to Top