Volume 43 Issue 4
May  2014
Turn off MathJax
Article Contents

Yin Jihao, Sun Jianying. Hyperspectral band reconstruction based on compressed sensing theory[J]. Infrared and Laser Engineering, 2014, 43(4): 1260-1264.
Citation: Yin Jihao, Sun Jianying. Hyperspectral band reconstruction based on compressed sensing theory[J]. Infrared and Laser Engineering, 2014, 43(4): 1260-1264.

Hyperspectral band reconstruction based on compressed sensing theory

  • Received Date: 2013-08-12
  • Rev Recd Date: 2013-09-13
  • Publish Date: 2014-04-25
  • Hyperspectral image processing had attracted high attention in remote sensing fields. One of the main issues was to address the problem of huge data and hard transmission via sampling and reconstruction. Compressed sensing theory was investigated in this paper for band reconstruction. Based on compressed sensing theory, original signal could be reconstructed efficiently without satisfying the Nyquist-Shannon criterion. Adjacent spectral bands of hyperspectral images were highly correlated, resulting in strong sparse representation. This significant property made it possible to obtain the whole spectrum information from limited bands of original hyperspectral data via compressed sensing theory. Experimental results demonstrate the feasibility and reliability of applying compressed sensing theory for sampling and reconstruction on bands of hyperspectral images. The proposed band reconstruction method can perform high correlation coefficients and low relative errors between a pair of reconstructed and original hyperspectral bands. Simultaneously, high levels of reconstruction efficiency are achieved, and reconstructed spectral curve is in accordance with original data as well.
  • [1] Xu Hong, Wang Xiangjun. Applications of multispectral/ hyperspectral imaging technologies in military [J]. Infrared and Laser Engineering, 2007, 36(1): 13-17. (in Chinese) 许洪, 王向军. 多光谱、超光谱成像技术在军事上的应用[J]. 红外与激光工程, 2007, (1): 13-17.
    [2]
    [3]
    [4] Yin J, Gao C, Jia X. Using Hurst and Lyapunov exponent for hyperspectral image feature extraction [J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 705-709.
    [5]
    [6] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [7] Xiao Longlong, Liu Kun, Han Dapeng, et al. Focal plane coding method for high resolution infrared imaging [J]. Infrared and Laser Engineering, 2011, 40(11): 2065-2070. (in Chinese) 肖龙龙, 刘昆, 韩大鹏, 等. 焦平面编码高分辨率红外成像 方法[J]. 红外与激光工程, 2011, 40(11): 2065-2070.
    [8]
    [9]
    [10] He Yuanlei, Liu Dazhi, Wang Jingli, et al. Independent component analysis-based band selection for hyperspectral imagery [J]. Infrared and Laser Engineering, 2012, 41 (3): 818-824. (in Chinese) 何元磊, 刘代志, 王静荔, 等. 利用独立成分分析的高光谱 图像波段选择方法[J]. 红外与激光工程, 2012, 41 (3): 818-824.
    [11] Wang L, Wu J. Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/ KLT [J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 587-591.
    [12]
    [13]
    [14] Cands E J. The restricted isometry property and its implications for compressed sensing [C]//C R Math Acad Sci Serie I, 2008, 346: 589-592.
    [15] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
    [16]
    [17] Yin J, Gao C, Jia X. Wavelet packet analysis and gray model for feature extraction of hyperspectral data [J]. Geoscience and Remote Sensing Letters, 2013, 10(4): 682-686.
    [18]
    [19] Yin Jihao, Sun Jianying, Wang Yisong, et al. Sample weighting constrained energy minimization algorithm[J]. Acta Electronica Sinica, 2012, 40(4): 788-792. (in Chinese) 尹继豪, 孙建颖, 王义松, 等. 样本加权约束能量最小化算 法[J]. 电子学报, 2012, 40(4): 788-792.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(477) PDF downloads(256) Cited by()

Related
Proportional views

Hyperspectral band reconstruction based on compressed sensing theory

  • 1. School of Astronautics,Beihang University,Beijing 100191,China

Abstract: Hyperspectral image processing had attracted high attention in remote sensing fields. One of the main issues was to address the problem of huge data and hard transmission via sampling and reconstruction. Compressed sensing theory was investigated in this paper for band reconstruction. Based on compressed sensing theory, original signal could be reconstructed efficiently without satisfying the Nyquist-Shannon criterion. Adjacent spectral bands of hyperspectral images were highly correlated, resulting in strong sparse representation. This significant property made it possible to obtain the whole spectrum information from limited bands of original hyperspectral data via compressed sensing theory. Experimental results demonstrate the feasibility and reliability of applying compressed sensing theory for sampling and reconstruction on bands of hyperspectral images. The proposed band reconstruction method can perform high correlation coefficients and low relative errors between a pair of reconstructed and original hyperspectral bands. Simultaneously, high levels of reconstruction efficiency are achieved, and reconstructed spectral curve is in accordance with original data as well.

Reference (19)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return