像元映射变分辨率光谱成像重构 -

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像元映射变分辨率光谱成像重构

doi:10.37188/CO.2022-0108

肖树林^{1, 2,},
胡长虹^1,,,
高路尧^{1, 2,},
颜克雄^{1, 2,},
杨春吉^{1, 2,},
李洪利^{1, 2,}

1.
中国科学院长春光学精密机械与物理研究所，吉林长春 130033
2.
中国科学院大学，北京 100049

基金项目:吉林省与中国科学院科技合作高技术产业化（No. 2020SYHZ0028）；（吉林省） 2021年省预算内基本建设资金（No. 2021C045-3）。

详细信息

作者简介:
肖树林（1996—），男，江西赣州人，硕士研究生，2020年于南昌航空大学获得学士学位，主要从事智能图像处理、计算光谱成像方面的研究。E-mail：13263073168@163.com
胡长虹（1982—），男，吉林长春人，副研究员，博士生导师，2013年于吉林大学获得博士学位，2012—2013年在美国西弗吉尼亚大学做访问学者，主要从事高光谱成像、计算成像、数据挖掘、软件质量评价方面的研究。E-mail：changhonghu@rocketmail.com

中图分类号:O438
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- 被引次数:0
出版历程
- 收稿日期:2022-05-30
- 修回日期:2022-06-22
- 网络出版日期:2022-08-03

Pixel mapping variable-resolution spectral imaging reconstruction

XIAO Shu-lin^{1, 2,},
HU Chang-hong^1,,,
GAO Lu-yao^{1, 2,},
YAN Ke-xiong^{1, 2,},
YANG Chun-ji^{1, 2,},
LI Hong-li^{1, 2,}

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds:This research is funded by the cooperation project between Jilin Province and Chinese Academy of Sciences (No. 2020SYHZ0028）； (Jilin Province) Capital construction funds within the provincial budget in 2021 (No. 2021C045-3).

More Information

Corresponding author:changhonghu@rocketmail.com

摘要

摘要:
本文讨论了随机滤光片光谱编码-解码的基本原理与重构方法，利用深度学习欠完备自编码器的自动特征提取机制，构建了高精度、低延时的像元映射变分辨率光谱成像重构网络，通过变换像元映射关系完成了2×2、4×4像元阵列光谱重构网络的并行训练。最后，利用512×512、120谱段(430 ~670 nm)的遥感光谱图像对重构网络进行验证，实现了2×2像元阵列/40谱段重构峰值信噪比达53 dB、均方误差小于0.002、重构用时0.87 s与4×4像元阵列/120谱段重构峰值信噪比达64 dB、均方误差小于10⁻⁵、重构用时0.52 s的变分辨率光谱图像重构。实验结果表明像元映射变分辨率光谱成像重构网络具备高精度、低延时的动态变换性能。
- 变分辨率光谱成像/
- 像元映射/
- 随机滤光片/
- 深度学习
Abstract:
In this paper, the basic principle and reconstruction method of random filter spectral coding-decoding are discussed. According to the automatic feature extraction mechanism of a deep learning undercomplete autoencoder, a pixel mapping variable-resolution spectral imaging reconstruction network with high reconstruction accuracy and low delay is constructed. The parallel training of a 2×2 and 4×4 pixel array spectral reconstruction network is implemented by transforming the pixel mapping relationship. Finally, the network’s performance is verified by the remote sensing data with 512×616 with 120 bands spectral images. For a 2×2 pixel array with 40 bands, the reconstruction PSNR is 53 dB, the reconstruction MSE is less than 0.002, and the reconstruction time is 0.85 s. For a 4×4 pixel array with 120 bands, the reconstruction PSNR is 64 dB, the reconstruction MSE is less than 10⁻⁵, and the reconstruction time is 0.5 s. The experimental results show that the pixel mapping variable-resolution spectral imaging reconstruction network has the dynamic transformation performance of high accuracy and low delay.
- variable-resolution spectral imaging/
- pixel mapping/
- random filter/
- deep learning

HTML全文

图 1压缩感知光谱编解码原理

Figure 1.Principle diagram of compressed sensing spectral encoding-decoding

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图 2深度学习光谱编解码原理

Figure 2.Principle diagram of deep learning spectral encoding-decoding

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图 3随机滤光片与光谱重构网络协同设计

Figure 3.Collaborative design of random filter and spectral reconstruction network

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图 4像元映射变分辨率光谱重构网络训练

Figure 4.Spectral reconstruction network training of pixel mapping variable resolution

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图 5像元级随机滤光片光谱成像

Figure 5.Pixel random filter spectral imaging

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图 6像元随机滤光片变分辨率动态转换示意

Figure 6.Variable resolution dynamic conversion of a pixel random filter

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图 7训练与验证损失曲线

Figure 7.Training and verification loss curve

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图 8随机滤光片透过率曲线

Figure 8.Transmission curves of random filter

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图 9样本外预测结果

Figure 9.Out-sample forecasting results

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图 10遥感高光谱图像验证结果Fig.10 Remote sensing hyperspectral image verification results

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