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收稿：2021-11-11，
修回：2022-1-19，
录用：2022-1-26，
纸质出版：2023-02-16
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目的
2
高光谱解混是高光谱遥感数据分析中的热点问题，其难点在于信息不充分导致的问题病态性。基于光谱库的稀疏性解混方法是目前的代表性方法，但是在实际情况中，高光谱数据通常包含高斯、脉冲和死线等噪声，且各波段噪声的强度往往不同，因此常用的稀疏解混方法鲁棒性不够，解混精度有待提高。针对该问题，本文对高光谱图像进行非负稀疏分量分解建模，提出了一种基于非负稀疏分量分析的鲁棒解混方法。
方法
2
首先综合考虑真实高光谱数据的混合噪声及其各波段噪声强度不同的统计特性，在最大后验概率框架下建立非负矩阵稀疏分量分解模型，然后采用
$$\ell_\text{1
1}$$
范数刻画噪声的稀疏性，
$$\ell_\text{2
0}$$
范数刻画丰度的全局行稀疏性，全变分（total variation，TV）正则项刻画像元的局部同质性和分段平滑性，建立基于非负稀疏分量分析的高光谱鲁棒解混优化模型，最后采用交替方向乘子法（alternating direction method of multipliers，ADMM）设计高效迭代算法。
结果
2
在2组模拟数据集上的实验结果表明，相比于5种对比方法，提出方法在信号与重建误差比（signal to reconstruction error，SRE）指标上比同类最好方法提升了4.11 dB；在不同噪声强度下，提出方法的SRE平均提升6.94 dB；在美国内华达州Cuprite矿区数据的真实数据实验中，提出方法也取得了较好的矿物解混性能。
结论
2
提出方法综合考虑了高斯随机噪声和稀疏性结构噪声对解混精度的影响，具有较好的混合噪声自适应稳健处理能力，解混的精度比现有同类方法更高。
Objective
2
Hyper-spectral remote sensing (RS) image based pixel issue is focused on the spectra of several elements-purified mixture in common restricted by the low spatial resolution of hyperspectral imagers and the diversity of spectral features in nature. The quality of hyper-spectral images (HSIs) is often constrained of mixed pixels. The development and application of RS-based hyper-spectral images is challenged for the constraints of mixed pixels. Hyper-spectral un-mixing problem is essential to hyper-spectral image analysis through decomposing mixed pixels into a set of pure substances (endmembers) and corresponding component ratios (abundances). The challenging issue of un-mixing is connected with insufficient and unmatched information problem. At present
the hyper-spectral un-mixing content is required to segment into three categories: 1) geometry-based
2) statistics-based
and 3) spectral profiles-based. The spectral profiling is relevant to a pre-collected spectral library composed of a large number of elements-purified spectra as an end-member dictionary. It is not necessary to extract end-members from hyper-spectral data
and the following influence of inaccurate end-member extraction can be avoided. Generally
the number of end-members in the spectral library is more than the end-members-related amount in a hyper-spectral image
which make richer matrix more sparsely. To improve the un-mixing accuracy
the constraints-sparsely is required to be added in terms of richer matrix. In addition
HIS-based spatial information can aid to the improvement of un-mixing accuracy greatly. In reality
hyper-spectral data quality is constrained of multiple noises because the photonic energy is limited in imaging process
such as Gaussian
impulse and deadline noise
and each band of noise-intensified. However
most of un-mixing problems are relevant to Gaussian noise and the other types of noise like impulses and deadlines are not involved in
and the assumption of Gaussian noise intensity is for the same bands of hyper-spectral data. In order to resolve the problems mentioned above
we develop non-negative sparse component decomposition
and a robust un-mixing to sparse component analysis (RUnSCA) for hyper-spectral images.
Method
2
First
the application of mixed noise of real hyperspectral data and the statistic features of different noise intensity of each band are involved in. Then
we carry out a non-negative sparse component-based model analysis in terms of the maximized posterior probability framework. The hyper-spectral data quality of each band is improved further when the mixed noise is as the linear combination of spectra in a profile. Considering the sparsity nature of impulse noise and deadline noise
the
$$\ell_\text{1
1}$$
norm is as the constraints of these noises. Since the number of endmembers in the spectral library is more than the number of end-members in the hyper-spectral image
a smaller number for non-zero rows can be existed in the abundance matrix
which means the abundance has global row sparsity. In order to analyze the sparsity of the abundance matrix more effectively
we introduce
$$\ell_\text{2
0}$$
norm constraint to the abundance matrix for mining the global row sparsity. In hyper-spectral images
adjacent pixels-local contains mutual substances
and their spectra should be similar mutually
so the image can show smoothness-piecewise. The abundance matrix is regarded as piecewise smooth as well because the elements in each column of the abundance matrix represent the proportion of corresponding objects in a pixel. The factor of total variation regularization (TV) is added to the model analysis. Finally
the proposed RUnSCA problem can be resolved in terms of the alternating direction multiplier method (ADMM).
Result
2
In order to verify the effectiveness of the proposed RUnSCA
we use two sorts of simulated data sets and one real data set in experiment. We use signal to reconstruction error (SRE) for evaluating the performance of un-mixing as well. There are five representative methods used in the comparative experiments. The methodology is based on collaborative sparse theory of those are variable-split-coordinated sparse un-mixing and augmented Lagrangian (CLSUnSAL) and sparse-cooridinated hyper-spectral un-mixing using
$$\ell_\text{0}$$
norm (CSUnL0). Sparse un-mixing problem is resolved via variable-split and Lagrangian-augmented and total variation (SUnSAL-TV)
and the row-sparsity spectral un-mixing is resolved via total variation (RSSUn-TV). Sparse un-mixing of hyper-spectral data with bandwise model (SUBM) is also be considered as the variation of Gaussian noise in different bands of hyper-spectral image and the impulse noise or deadlines is taken into account. On the two simulated data sets
in comparison with the best result of five popular methods
the experiments show that our SRE result can improve 4.11 dB and 6.94 dB on average under various noise intensities. It illustrates the proposed method can deal with it more robustness. The RUnSCA also achieves good performance of mineral un-mixing on real data over Cuprite mine in Nevada
USA. The un-mixing performance-validated is based on a single mixed spectrum as well. The results show that the spectrum-reconstructed is closest to the real spectrum with the lowest RMSE. The RUnSCA can analyze the composition and proportion of the mixed spectra accurately. The experiments on simulated data and real data show that our analysis has more robustness over mixed noise.
Conclusion
2
This non-negative sparse component decomposition task demonstrate that a robust un-mixing for hyper-spectral images is effective with sparse component analysis (RUnSCA)
which can comprehensively considers the influence of Gaussian random noise and sparse structural noise on the accuracy of linear un-mixing. The potential ADMM problem can be resolved further.
Bateson C A, Asner G P and Wessman C A. 2000. Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 1083-1094[DOI: 10.1109/36.841987]
Bioucas-Dias J M. 2009. A variable splitting augmented Lagrangian approach to linear spectral unmixing//Proceedings of the 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Grenoble, France: IEEE: 1-4[ 10.1109/WHISPERS.2009.5289072 DOI: http://dx.doi.org/10.1109/WHISPERS.2009.5289072 ]
Bioucas-Dias J M and Figueiredo M A T. 2010. Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing//Proceedings of the 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Reykjavik, Iceland: IEEE: 1-4[ DOI: 10.1109/WHISPERS.2010.5594963 http://dx.doi.org/10.1109/WHISPERS.2010.5594963 ]
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]
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]
Bruckstein A M, Elad M and Zibulevsky M. 2008. On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations. IEEE Transactions on Information Theory, 54(11): 4813-4820[DOI: 10.1109/TIT.2008.929920]
Chen J, Ma L, Chen X H and Rao Y H. 2016. Research progress of spectral mixture analysis. Journal of Remote Sensing, 20(5): 1102-1109
陈晋, 马磊, 陈学泓, 饶玉晗. 2016. 混合像元分解技术及其进展. 遥感学报, 20(5): 1102-1109[DOI: 10.11834/jrs.20166169]
Chen S X and Chu C Q. 2019. Hyperspectral unmixing based on sparse and orthogonal constrained non-negative matrix factorization. Journal of Computer Applications, 39(8): 2276-2280
陈善学, 储成泉. 2019. 基于稀疏和正交约束非负矩阵分解的高光谱解混. 计算机应用, 39(8): 2276-2280[DOI: 10.11772/j.issn.1001-9081.2019010105]
Gasso G, Rakotomamonjy A and Canu S. 2009. Recovering sparse signals with a certain family of nonconvex penalties and DC programming. IEEE Transactions on Signal Processing, 57(12): 4686-4698[DOI: 10.1109/TSP.2009.2026004]
Heinz D C and Chang C I. 2001. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(3): 529-545[DOI: 10.1109/36.911111]
Huang W, Wu F Y and Sun L. 2020. Hyperspectral sparse unmixing based on local weighted low-rank prior. Journal of Applied Sciences, 38(6): 890-905
黄伟, 伍飞扬, 孙乐. 2020. 基于局部加权低秩先验的高光谱稀疏解混方法. 应用科学学报, 38(6): 890-905[DOI: 10.3969/j.issn.0255-8297.2020.06.006]
Iordache M D, Bioucas-Dias J M and Plaza A. 2011. Sparse unmixing of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 49(6): 2014-2039[DOI: 10.1109/TGRS.2010.2098413]
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]
Iordache M D, Bioucas-Dias J M and Plaza A. 2014. Collaborative sparse regression for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 341-354[DOI: 10.1109/TGRS.2013.2240001]
Jia Q, Liao S Y, Zhang Z Y and Yang X J. 2020. Reweighted sparse nonnegative matrix decomposition for hyperspectral unmixing. Infrared and Laser Engineering, 49(S2): #20200152
贾麒, 廖守亿, 张作宇, 杨薪洁. 2020. 重加权稀疏非负矩阵分解的高光谱解混. 红外与激光工程, 49(S2): #20200152[DOI: 10.3788/irla20200152]
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 ofRemote 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]
Li C, Liu Y, Cheng J, Song R C, Ma J Y, Sui C and Chen X. 2020. Sparse unmixing of hyperspectral data with bandwise model. Information Sciences, 512: 1424-1441[DOI: 10.1016/j.ins.2019.10.036]
Li J and Bioucas-Dias J M. 2009. Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data//2008 IEEE International Geoscience and Remote Sensing Symposium. Boston, USA: IEEE: Ⅲ-250-Ⅲ-253[ DOI: 10.1109/IGARSS.2008.4779330 http://dx.doi.org/10.1109/IGARSS.2008.4779330 ]
Miao L D and Qi H R. 2007. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 45(3): 765-777[DOI: 10.1109/TGRS.2006.888466]
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]
Seneviratne A J. 2012. L0 Sparse Signal Processing and Model Selection with Applications. Sydney, Australia: University of New South Wales
Shi Z W, Shi T Y, Zhou M and Xu X. 2018. Collaborative sparse hyperspectral unmixing using l 0 norm. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5495-5508[DOI: 10.1109/TGRS.2018.2818703].
Song X R, Wu L D and Meng X L. 2020. Unsupervised hyperspectral unmixing based on robust non-negative matrix factorization. Journal of Image and Graphics, 25(4): 801-812
宋晓瑞, 吴玲达, 孟祥利. 2020. 利用稳健非负矩阵分解实现无监督高光谱解混. 中国图象图形学报, 25(4): 801-812[DOI: 10.11834/jig.190354]
Sun L, Zhan T M, Wu Z B and Jeon B. 2018. A novel 3D anisotropic total variation regularized low rank method for hyperspectral image mixed denoising. ISPRS International Journal of Geo-Information, 7(10): 412[DOI: 10.3390/ijgi7100412]
Wang J J, Huang T Z, Huang J, Dou H X, Deng L J and Zhao X L. 2019. Row-sparsity spectral unmixing via total variation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 5009-5022[DOI: 10.1109/JSTARS.2019.2950700]
Winter M E. 1999. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data//Proceedings Volume 3753, Imaging Spectrometry V. Denver, United States: SPIE: 266-275[ DOI: 10.1117/12.366289 http://dx.doi.org/10.1117/12.366289 ]
Yuan J, Zhang Y J and Gao F P. 2018. An overview on linear hyperspectral unmixing. Journal of Infrared and Millimeter Waves, 37(5): 553-571
袁静, 章毓晋, 高方平. 2018. 线性高光谱解混模型综述. 红外与毫米波学报, 37(5): 553-571[DOI: 10.11972/j.issn.1001-9014.2018.05.008]
Zhang B. 2016. Advancement of hyperspectral imageprocessing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090
张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报, 20(5): 1062-1090[DOI: 10.11834/jrs.20166179]
Zhang H Y, He W, Zhang L P, Shen H F and Yuan Q Q. 2014. Hyperspectral image restoration using low-rank matrix recovery. IEEE Transactions on Geoscience and RemoteSensing, 52(8): 4729-4743[DOI: 10.1109/TGRS.2013.2284280]
Zhang L P and Li J Y. 2016. Development and prospect of sparse representation-based hyperspectral image processing and analysis. Journal of Remote Sensing, 20(5): 1091-1101
张良培, 李家艺. 2016. 高光谱图像稀疏信息处理综述与展望. 遥感学报, 20(5): 1091-1101[DOI: 10.11834/jrs.20166050]
Zhao Y Q and Yang J X. 2015. Hyperspectral image denoising via sparse representation and low-rank constraint. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 296-308[DOI: 10.1109/TGRS.2014.2321557]
Zhu L, Li M, Qin K and Pan C Y. 2020. Review of research progress on mineral hyperspectral unmixing. Remote Sensing Information, 35(3): 15-23
朱玲, 李明, 秦凯, 潘澄雨. 2020. 矿物高光谱解混进展研究综述. 遥感信息, 35(3): 15-23[DOI:10.3969/j.issn.1000-3177.2020.03.002]
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