基于多视场狭缝的高效信息获取光谱成像系统
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国科大杭州高等研究院

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Efficient information acquisition spectral imaging system based on multi-field slits
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Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences

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    摘要:

    视频光谱成像技术是目前遥感探测发展的一个重要方向,可以实现4维信息获取(两维空间+光谱+时间),对于动态目标探测等应用场景有十分重要的意义。目前的技术手段是以滤光片方式为主,不具备光栅作为分光元件时的高光谱分辨率优势,基于此,提出了非耦合狭缝阵列扫描光谱成像(uSASHI)和 编码狭缝阵列扫描光谱成像(cSASHI)系统。通过增加狭缝数目的方式,实现同一时刻多个视场信息的获取,极大的提升信息获取效率。uSASHI的每个狭缝获取信息之间不会耦合,n 条狭缝可以实现 n 倍的信息获取效率的提升,cSASHI 的狭缝按照压缩感知理论排列,可以实现欠采样条件下(采样率α≤1)视频光谱成像,信息获取效率可以提升n/α倍。本文设计的系统最终实现了1024*496*30的光谱数据立方体10Hz视频光谱成像方式,cSASHI实现了更高帧频。所提系统为视频光谱成像技术提供了新的方向,为未来动态目标探测等应用打下了基础。

    Abstract:

    Video spectral imaging technology is an important direction in the development of remote sensing detection. It can achieve 4-dimensional information acquisition (two-dimensional space + spectrum + time), which is of great significance for application such as dynamic target detection. The current technical means are mainly based on the filter method, and do not have the high-resolution advantage of grating. Uncoupled Slit Array Scan Hyperspectral imager (uSASHI) and Coded Slit Array Scan Hyperspectral imager (cSASHI) are proposed in this paper, both use multiple slits to achieve simultaneous acquisition of multiple fields of view information to improve the information acquisition rate, and enables video-level spectral imaging. The information obtained by each slit of uSASHI will not be coupled, and n slits can achieve n times the improvement of information acquisition efficiency. The slits of cSASHI are arranged according to the compressed sensing theory, which can achieve under-sampling conditions (sampling rate α≤ 1) video spectral imaging, the information acquisition efficiency can be improved by n/α times. The system designed in this paper finally realizes the 1024*490*30 spectral data cube 10Hz video spectral imaging method, and cSASHI achieves a higher frame rate. The proposed system provides a new direction for the video spectral imaging technology and lays a better foundation for future applications.

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  • 收稿日期:2022-06-21
  • 最后修改日期:2022-07-01
  • 录用日期:2022-07-04
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