动态测量的高光谱图像压缩感知

闫歌; 许廷发; 马旭; 张宇寒; 王茜; 谭翠媚

doi:10.3788/CO.20181104.0550

Volume 11 Issue 4

Jul. 2018

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Chinese Optics > 2018 > 11(4): 550-559.

YAN Ge, XU Ting-fa, MA Xu, ZHANG Yu-han, WANG Xi, TAN Cui-mei. Hyperspectral image compression sensing based on dynamic measurement[J]. Chinese Optics, 2018, 11(4): 550-559. doi: 10.3788/CO.20181104.0550

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YAN Ge, XU Ting-fa, MA Xu, ZHANG Yu-han, WANG Xi, TAN Cui-mei. Hyperspectral image compression sensing based on dynamic measurement[J]. Chinese Optics, 2018, 11(4): 550-559. doi: 10.3788/CO.20181104.0550

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Hyperspectral image compression sensing based on dynamic measurement

doi: 10.3788/CO.20181104.0550

School of Optoelectronics, MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China

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Major Science Instrument Program of the National Natural Science Foundation of China 61527802

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Corresponding author: XU Ting-fa, E-mail:ciom_xtf1@bit.edu.cn; MA Xu, E-mail:maxu@bit.edu.cn
Received Date: 01 Nov 2017
Rev Recd Date: 05 Jan 2018
Publish Date: 01 Aug 2018

Abstract

Abstract

Based on the strong spectral correlation between adjacent bands of hyperspectral images, we proposes a compressed sensing algorithm that uses edge information to design dynamic measurement rate to improve the reconstruction effect of compressive sensing in hyperspectral images. First, each image block is sampled at a fixed measurement rate by a random projection block-compressive sensing method, a single-band image is reconstructed as a priori information of other bands, and an image edge region is extracted therefrom; then, the measurement values are adaptively assigned according to the richness of the edge information of each image block. With a certain total number of measurements, different number of measurements is assigned to different image blocks. Finally, the remaining wave bands are collected and reconstructed with the assigned measurements. The simulation results show that under the same total number of measurements, the hyperspectral image quality(Peak Signal to Noise Ratio(PSNR)) reconstructed by the dynamic measurement algorithm proposed in this paper is 1-4 dB higher than the traditional fixed-measurement compressive sensing strategy. Moreover, the reconstruction time is also reduced, and the image quality at the detail is further enhanced based on the successful reconstruction of the hyperspectral images.
- hyperspectral image,
- compressive sensing,
- dynamic measurement rate,
- inter-spectral correlation

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References

[1]	童庆禧, 张兵, 张立福.中国高光谱遥感的前沿进展[J].遥感学报, 2016, 20(5):689-707. http://www.opticsjournal.net/abstract.htm?id=OJ140102000067SpVrYu TONG Q X, ZHANG B, ZHANG L F. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5):689-707.(in Chinese) http://www.opticsjournal.net/abstract.htm?id=OJ140102000067SpVrYu
[2]	王忠良, 冯燕, 肖华, 等.高光谱图像的分布式压缩感知成像与重构[J].光学精密工程, 2015, 23(4):1131-1137. http://www.cnki.com.cn/Article/CJFDTotal-GXJM201504028.htm WANG ZH L, FENG Y, XIAO H, et al.. Distributed compressive sensing imaging and reconstruction of hyperspectral imagery[J]. Opt. Precision Eng., 2015, 23(4):1131-1137.(in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-GXJM201504028.htm
[3]	汤毅, 万建伟, 粘永健.高光谱图像的分布式近无损压缩[J].光学学报, 2015, 35(3):0928001. http://www.cnki.com.cn/Article/CJFDTotal-GXXB201503010.htm TANG Y, WAN J W, NIAN Y J. Distributed nearlossless compression of hyperspectral images[J]. Acta Optica Sinica, 2015, 35(3):0928001.(in Chinese) http://www.cnki.com.cn/Article/CJFDTotal-GXXB201503010.htm
[4]	刘长虹, 夏青.高光谱成像在肉品品质及安全检测方面的应用[J].合肥工业大学学报(自然科学版), 2016, 39(4):554-559. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hfgydxxb201604024 LIU CH H, XIA Q. Application of hyperspectral imaging in evaluating quality and safety of meat[J]. Journal of Hefei University of Technology, 2016, 39(4):554-559.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hfgydxxb201604024
[5]	梅风华, 李超, 张玉鑫.光谱成像技术在海域目标探测中的应用[J].中国光学, 2017, 10(6):708-718. http://www.chineseoptics.net.cn/CN/abstract/abstract9565.shtml MEI F H, LI CH, ZHANG Y X. Application of spectral imaging technology in maritime target detection[J]. Chinese Optics, 2017, 10(6):708-718.(in Chinese) http://www.chineseoptics.net.cn/CN/abstract/abstract9565.shtml
[6]	张达, 郑玉权.高光谱遥感的发展与应用[J].光学与光电技术, 2013, 11(3):67-73. https://www.wenkuxiazai.com/doc/5f6a79dbf524ccbff12184ea.html ZHANG D, ZHENG Y Q.Hyperspectral remote sensing and its development and application review[J]. Optics & Optoelectronic Technology, 2013, 11(3):67-73.(in Chinese) https://www.wenkuxiazai.com/doc/5f6a79dbf524ccbff12184ea.html
[7]	王跃明, 韦丽清, 郎均慰, 等.先进焦平面与光谱成像技术现状[J].光学与光电技术, 2014, 12(1):7-13. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hwyhmb201501010 WANG Y M, WEI L Q, LANG J W, et al.. Status of advanced focal plane arrays and spectral imaging technology[J]. Optics & Optoelectronic Technology, 2014, 12(1):7-13.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hwyhmb201501010
[8]	马芳苑, 王跃明.基于CMOS的轻小型高光谱仪的发展及应用[J].光学与光电技术, 2016, 14(4):84-89. https://www.researchgate.net/profile/Yong_Yao2/publication/271831506_Characteristics_of_typical_optical_systems_with_diffractive_gratings_of_micro-spectrometers_and_improvement_of_spectrometer's_resolution/links/54ffc99a0cf2eaf210bc334d.pdf?inViewer=true&pdfJsDownload=true&disableCoverPage=true&origin=publication_detail MA F Y, WANG Y M. Development and application of the light and small CMOS hyperspectral camera[J]. Optics & Optoelectronic Technology, 2016, 14(4):84-89.(in Chinese) https://www.researchgate.net/profile/Yong_Yao2/publication/271831506_Characteristics_of_typical_optical_systems_with_diffractive_gratings_of_micro-spectrometers_and_improvement_of_spectrometer's_resolution/links/54ffc99a0cf2eaf210bc334d.pdf?inViewer=true&pdfJsDownload=true&disableCoverPage=true&origin=publication_detail
[9]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. doi: 10.1109/TIT.2006.871582
[10]	CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transaction on Information Theory, 2006, 52(2):489-508. doi: 10.1109/TIT.2005.862083
[11]	BIOUCAS-DIA J M, FIGUEIREDO M A T. A newTwIST:two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12):2992-3004. doi: 10.1109/TIP.2007.909319
[12]	WRIGHT S J, NOWAK R D, FIGUEIREDO M A T. Sparse reconstruction by separable approximation[J]. IEEE Transactions on Signal Processing, 2009, 57(7):2479-2493. doi: 10.1109/TSP.2009.2016892
[13]	蒋沅, 苗生伟, 罗华柱, 等.Lp范数压缩感知图像重建优化算法[J].中国图象图形学报, 2017, 22(4):0435-0442. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201704003.htm JIANG Y, MIAO SH W, LUO H ZH, et al.. Improved search algorithm for compressive sensing image recovery based on Lp norm[J]. Journal of Image and Graphics, 2017, 22(4):0435-0442.(in Chinese) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201704003.htm
[14]	WANG A, LIU L, ZENG B, et al.. Progressive image coding based on an adaptive block compressed sensing[J]. IEICE Electronics Express, 2011, 8(8):575-581. doi: 10.1587/elex.8.575
[15]	CHEN H W, KANG L W, LU CH S. Dynamic measurement rate allocation for distributed compressive video sensing[J]. Proc. of SPIE, 2010, 7744:1117-1126. https://core.ac.uk/display/52370203
[16]	左觅文, 常侃, 施静兰, 等.基于帧间相关性的自适应采样率分块视频压缩感知[J].电讯技术, 2015, 55(4):360-365. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201504002 ZUO M W, CHANG K, SHI J L, et al.. Adaptive rate block compressive video sensing based on inter-frame correlation[J]. Telecommunication Engineering, 2015, 55(4):360-365.(in Chinese) http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201504002
[17]	HOU Y, ZHANG Y N. Interband prediction compressed sensing reconstruction algorithm for hyperspectral image[C]. 20164th International Workshop on Earth Observation and Remote Sensing Applications(EORSA), IEEE, Guangzhou, China, 2016: 465-469.
[18]	王相海, 宋海鹰, 宋传鸣, 等.谱间和帧内协同稀疏的高光谱图像压缩感知模型[J].中国科学:信息科学, 2016, 46(3):361-375. http://scis.scichina.com/cn/2016/N112014-00402.pdf WANG X H, SONG H Y, SONG CH M, et al.. Hyperspectral image compressed sensing model based on the collaborative sparsity of the intra-frame and inter-band[J]. Scientia Sinica Informations, 2016, 46(3):361-375.(in Chinese) http://scis.scichina.com/cn/2016/N112014-00402.pdf
[19]	HOU Y, LIU J. Hyperspectral image compressed sensing based on effective spectral reconstruction[C]. 2016 International Symposium on Computer, Consumer and Control(IS3C), IEEE, Xi'an, China, 2016: 660-663.

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