收稿：2019-06-26，
纸质出版：2020-03-07
移动端阅览
高光谱压缩感知（HCS）对于解决机载或星载高光谱数据的存储与实时传输具有重要意义。目前，线性混合模型（LMM）已被成功应用于HCS；然而，由于光照条件、地形变化以及大气作用等的影响，所获取的地物光谱会发生扰动，从而限制了HCS重建质量的提高。在LMM基础上，通过引入光谱修正项来修正光谱扰动，提出了光谱扰动修正的LMM（SPC_LMM）；在此基础上，进一步提出了基于SPC_LMM的HCS（HCS_SPC_LMM）方法。该方法在采样端仅对原始高光谱图像进行光谱维压缩采样，基于压缩采样数据，将SPC_LMM应用HCS的重建，利用交替方向乘子法（ADMM）分别估计SPC_LMM中各分量的最优值，以获得最优的高光谱图像重建质量。实验结果表明，HCS_SPC_LMM能够获得优于其他典型HCS方法的重建质量。
Hyperspectral Compressed Sensing (HCS) is crucial for data storage and the real-time transmission of airborne- or spaceborne-based imaging platforms. The Linear Mixing Model (LMM) has been successfully applied to HCS reconstruction. However
the obtained spectrum may be disturbed
thereby limiting the improvement of reconstruction quality due to the influence of illumination conditions
topographic changes
and atmospheric effects. Spectral disturbance is corrected on the basis of LMM by introducing the spectral correction term
and a linear mixing model for spectral perturbation correction is proposed. Moreover
an improved HCS method based on modified LMM is proposed. This proposed model only performs spectral compressed sampling on the original hyperspectral images at the sampling end. The proposed method uses the proposed spectral perturbation correction model to reconstruct the original hyperspectral images based on the compressed sampling data. The alternating direction multiplier method is used to estimate the optimal values of each component in the modified LMM to obtain the optimal reconstruction quality. Experimental results show that the proposed method can achieve better reconstructed performance compared with other classical HCS methods.
Bioucas-Dias J M and Nascimento J M P . 2008 . Hyperspectral subspace identification . IEEE Transactions on Geoscience and Remote Sensing , 46 ( 8 ): 2435 - 2445 [ DOI: 10.1109/TGRS.2008.918089 http://dx.doi.org/10.1109/TGRS.2008.918089 ]
Bioucas-Dias J M , Plaza A , Dobigeon N , Parente M , Du Q , Gader P and Chanussot J . 2012 . Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 5 ( 2 ): 354 - 379 [ DOI: 10.1109/JSTARS.2012.2194696 http://dx.doi.org/10.1109/JSTARS.2012.2194696 ]
Boyd S , Parikh N , Chu E , Peleato B and Eckstein J . 2010 . Distributed optimization and statistical learning via the alternating direction method of multipliers . Foundations and Trends® in Machine Learning , 3 ( 1 ): 1 - 122 [ DOI: 10.1561/2200000016 http://dx.doi.org/10.1561/2200000016 ]
Chen C , Li W , Tramel E W and Fowler J E . 2014 . Reconstruction of hyperspectral imagery from random projections using multihypothesis prediction . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 1 ): 365 - 374 [ DOI: 10.1109/TGRS.2013.2240307 http://dx.doi.org/10.1109/TGRS.2013.2240307 ]
Drumetz L , Veganzones M A , Henrot S , Phlypo R , Chanussot J and Jutten C . 2016 . Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability . IEEE Transactions on Image Processing , 25 ( 8 ): 3890 - 3905 [ DOI: 10.1109/TIP.2016.2579259 http://dx.doi.org/10.1109/TIP.2016.2579259 ]
Feng Y , Wang Z L and Wang L . 2015 . A double compressed sensing model of hyperspectral imagery . Acta Aeronautica et Astronautica Sinica , 36 ( 9 ): 3041 - 3049
冯燕 , 王忠良 , 王丽 . 2015 . 一种高光谱图像的双压缩感知模型 . 航空学报 , 36 ( 9 ): 3041- 3049 ） [ DOI: 10.7527/S1000-6893.2014.0350 http://dx.doi.org/10.7527/S1000-6893.2014.0350 ]
Fowler J E . 2009 . Compressive-projection principal component analysis . IEEE Transactions on Image Processing , 18 ( 10 ): 2230 - 2242 [ DOI: 10.1109/TIP.2009.2025089 http://dx.doi.org/10.1109/TIP.2009.2025089 ]
Hong D F , Yokoya N , Chanussot J and Zhu X X . 2019 . An augmented linear mixing model to address spectral variability for hyperspectral unmixing . IEEE Transactions on Image Processing , 28 ( 4 ): 1923 - 1938 [ DOI: 10.1109/TIP.2018.2878958 http://dx.doi.org/10.1109/TIP.2018.2878958 ]
Iordache M D , Bioucas-Dias J M and Plaza A . 2012 . Total variation spatial regularization for sparse hyperspectral unmixing . IEEE Transactions on Geoscience and Remote Sensing , 50 ( 11 ): 4484 - 4502 [ DOI: 10.1109/TGRS.2012.2191590 http://dx.doi.org/10.1109/TGRS.2012.2191590 ]
Jia Y B , Feng Y and Wang Z L . 2015 . Reconstructing hyperspectral images from compressive sensors via exploiting multiple priors . Spectroscopy Letters , 48 ( 1 ): 22 - 26 [ DOI: 10.1080/00387010.2013.850727 http://dx.doi.org/10.1080/00387010.2013.850727 ]
Lan J H , Zou J L , Hao Y S , Zeng Y L , Zhang Y Z and Dong M W . 2018 . Research progress on unmixing of hyperspectral remote sensing imagery . Journal of Remote Sensing , 22 ( 1 ): 13 - 27
蓝金辉 , 邹金霖 , 郝彦爽 , 曾溢良 , 张玉珍 , 董铭巍 . 2018 . 高光谱遥感影像混合像元分解研究进展 . 遥感学报 , 22 ( 1 ): 13- 27 ）[ DOI: 10.11834/jrs.20186502 http://dx.doi.org/10.11834/jrs.20186502 ]
Ly N H , Du Q and Fowler J E . 2013 . Reconstruction from random projections of hyperspectral imagery with spectral and spatial partitioning . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 6 ( 2 ): 466 - 472 [ DOI: 10.1109/JSTARS.2012.2217942 http://dx.doi.org/10.1109/JSTARS.2012.2217942 ]
Martín G and Bioucas-Dias J M . 2016 . Hyperspectral blind reconstruction from random spectral projections . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 ( 6 ): 2390 - 2399 [ DOI: 10.1109/JSTARS.2016.2541541 http://dx.doi.org/10.1109/JSTARS.2016.2541541 ]
Martín G , Bioucas-Dias J M and Plaza A . 2015 . HYCA: a new technique for hyperspectral compressive sensing . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 5 ): 2819 - 2831 [ DOI: 10.1109/TGRS.2014.2365534 http://dx.doi.org/10.1109/TGRS.2014.2365534 ]
Mun S and Fowler J E . 2009 . Block compressed sensing of images using directional transforms // Proceedings of the 2009 16th IEEE International Conference on Image Processing . Cairo, Egypt : IEEE : 3021 - 3024 [ DOI: 10.1109/ICIP.2009.5414429 http://dx.doi.org/10.1109/ICIP.2009.5414429 ]
Nascimento J M P and Dias J M B . 2005 . Vertex component analysis: a fast algorithm to unmix hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 43 ( 4 ): 898 - 910 [ DOI: 10.1109/TGRS.2005.844293 http://dx.doi.org/10.1109/TGRS.2005.844293 ]
Shu X B and Ahuja N . 2011 . Imaging via three-dimensional compressive sampling (3DCS)//Proceedings of 2011 International Conference on Computer Vision . Barcelona, Spain : IEEE : 439 - 446 [ DOI: 10.1109/ICCV.2011.6126273 http://dx.doi.org/10.1109/ICCV.2011.6126273 ]
Song J T , Pan J , Xing L X , Jiang L J , Sun Y H , Zhang X F , Zhong W J and Fan B W . 2019 . Applicable observational scale analysis of Linear Spectral Mixture Model . Journal of Remote Sensing , 23 ( 2 ): 262 - 279
宋江涛 , 潘军 , 邢立新 , 蒋立军 , 孙也涵 , 张雪峰 , 仲伟敬 , 范博文 . 2019 . 线性光谱混合模型的适用观测尺度分析 . 遥感学报 , 23 ( 2 ): 262- 279 [ DOI: 10.11834/jrs.20198244 http://dx.doi.org/10.11834/jrs.20198244 ]
Sun Y B , Wu Z B , Wu M and Liu Q S . 2014 . Compressed sensing reconstruction of hyperspectral imagery jointly using low rank and sparse prior . Acta Electronica Sinica , 42 ( 11 ): 2219 - 2224
孙玉宝 , 吴泽彬 , 吴敏 , 刘青山 . 2014 . 联合低秩与稀疏先验的高光谱图像压缩感知重建 . 电子学报 , 42 ( 11 ): 2219- 2224 ） [ DOI: 10.3969/j.issn.0372-2112.2014.11.014 http://dx.doi.org/10.3969/j.issn.0372-2112.2014.11.014 ]
Thouvenin P A , Dobigeon N and Tourneret J Y . 2016 . Hyperspectral unmixing with spectral variability using a perturbed linear mixing model . IEEE Transactions on Signal Processing , 64 ( 2 ): 525 - 538 [ DOI: 10.1109/TSP.2015.2486746 http://dx.doi.org/10.1109/TSP.2015.2486746 ]
Tong Q X , Zhang B and Zhang L F . 2016 . Current progress of hyperspectral remote sensing in China . Journal of Remote Sensing , 20 ( 5 ): 689 - 707
童庆禧 , 张兵 , 张立福 . 2016 . 中国高光谱遥感的前沿进展 . 遥感学报 , 20 ( 5 ): 689- 707 ）[ DOI: 10.11834/jrs.20186502 http://dx.doi.org/10.11834/jrs.20186502 ]
Wang L , Feng Y , Gao Y L , Wang Z L and He M Y . 2018 . Compressed sensing reconstruction of hyperspectral images based on spectral unmixing . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 4 ): 1266 - 1284 [ DOI: 10.1109/JSTARS.2017.2787483 http://dx.doi.org/10.1109/JSTARS.2017.2787483 ]
Wang Y , Lin L , Zhao Q , Yue T W , Meng D Y and Leung Y . 2017 . Compressive sensing of hyperspectral images via joint tensor tucker decomposition and weighted total variation regularization . IEEE Geoscience and Remote Sensing Letters , 14 ( 12 ): 2457 - 2461 [ DOI: 10.1109/LGRS.2017.2771212 http://dx.doi.org/10.1109/LGRS.2017.2771212 ]
Wang Z L , Feng Y and Jia Y B . 2015 . Spatio-spectral hybrid compressive sensing of hyperspectral imagery . Remote Sensing Letters , 6 ( 3 ): 199 - 208 [ DOI: 10.1080/2150704X.2015.1024892 http://dx.doi.org/10.1080/2150704X.2015.1024892 ]
Wang Z L , Feng Y and Wang L . 2014 . Compressive sensing imaging and reconstruction of pushbroom hyperspectra . Optics and Precision Engineering , 22 ( 11 ): 3129 - 3135
王忠良 , 冯燕 , 王丽 . 2014 . 推扫式高光谱谱间压缩感知成像与重构 . 光学精密工程 , 22 ( 11 ): 3129- 3135 ） [ DOI: 10.3788/OPE.20142211.3129 http://dx.doi.org/10.3788/OPE.20142211.3129 ]
Wang Z and Xiao H . 2019 . Hyperspectral Compressive Sensing Using Improved Linear Mixing Model // Proceedings of 2019 3rd International Conference on Imaging , Signal Processing and Communication. Singapore, Singapore : IEEE : 52 - 56 [ 10 . 1109/ICISPC.2019.8935773 ]
Yuan B . 2018 . NMF hyperspectral unmixing algorithm combined with spatial and spectral . Journal of Remote Sensing , 22 ( 2 ): 265 - 276
袁博 . 2018 . 空间与谱间相关性分析的NMF高光谱解混 . 遥感学报 , 22 ( 2 ): 265- 276 [ DOI: 10.11834/jrs.20186445 http://dx.doi.org/10.11834/jrs.20186445 ]
Zhang L , Wei W , Tian C N , Li F and Zhang Y N . 2016 . Exploring structured sparsity by a reweighted laplace prior for hyperspectral compressive sensing . IEEE Transactions on Image Processing , 25 ( 10 ): 4974 - 4988 [ DOI: 10.1109/TIP.2016.2598652 http://dx.doi.org/10.1109/TIP.2016.2598652 ]
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