收稿：2016-12-19，
录用：2017-9-13，
纸质出版：2018-03
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非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征，改善基于NMF的高光谱解混算法性能，提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题，引入并结合高光谱图像两种典型的相关性特征，具体包括：基于马尔可夫随机场(MRF)模型，建立描述相邻像元空间相关特征的约束；通过复杂度映射技术，建立描述相邻波段谱间相关(光谱分段平滑)特征的约束；并将上述两种约束同时引入NMF解混目标函数中。实验结果表明，对于一般自然地物场景或人造地物场景，相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法，该算法可进一步提高高光谱解混精度；对于空间相关或谱间相关特征中某一种不显著的特殊场景，也具有更好的适应能力。通过将空间相关和谱间相关特征相结合，较全面地反映了高光谱数据与解混相关的重要特征，能够对绝大多数真实高光谱数据进行高精度解混，对高光谱解混及后续应用领域相关研究均具有参考价值。
The low spatial resolution of hyperspectral images always leads to the “mixed pixel” problem
where multiple objects exist in one pixel. The unmixing of mixed pixels is vital to the quantitative application of hyperspectral remote sensing. Non-negative matrix factorization (NMF) is a hot spot in hyperspectral unmixing research because of its non-negative constraints and capability to process high-dimensional data. However
NMF-based unmixing algorithms are ineffective because of universality
local minima
and other restrictions; additional specific features of hyperspectral images should be explored. Hyperspectral remote sensing images have significant correlation features
such as spatial and spectral correlation. The present study aims to improve the unmixing effect by adopting correlation features of hyperspectral remote sensing images. An NMF-based hyperspectral unmixing algorithm with spatial and spectral correlation analyses
named NMF hyperspectral unmixing algorithm based on spatial and spectral correlation (NMFSSC)
is proposed to address the universality and local minima problems of NMF. The proposed method adopts the correlation features of hyperspectral data in the unmixing process as the spatial correlation constraint of building adjacent pixels. The Markov Random Field (MRF) model and spectral correlation (piecewise smoothness) are used as constraints in building adjacent bands with complexity mapping technique. In this algorithm
the spatial correlation constraint of adjacent pixels is processed as the parallel and alternate step of the standard NMF object function. The spectral correlation of the adjacent bands is built as a new inner constraint of the standard function. During each iteration of the unmixing procedure
the distribution error of endmembers can be revised by the MRF-based spatial correlation constraint. The local minima problem can be revised by the complexity mapping constraint
thus potentially improving overall unmixing precision. Actual hyperspectral data with high and low spatial correlation are adopted in the experiments. Three algorithms
namely
Minimum Volume Constrained Nonnegative Matrix Factorization (MVCNMF)
Piecewise Smoothness NMF with Sparseness Constraints (PSNMFSC)
and NMF with Alternating Projected Subgradients (APSNMF)
are used with NMFSSC in the experiments. Results indicate that the proposed NMFSSC algorithm can improve unmixing precision
especially when dealing with high-spatial-correlation test data. The unmixing precision of NMFSSC is lower on the low-spatial-correlation data than on the high-spatial-correlation set
but the method still shows certain advantages over the three reference algorithms. Therefore
NMFSSC has a wide-ranging application scope. In summary
the proposed algorithm can increase the unmixing accuracy of most actual hyperspectral remote sensing data
especially those with high spatial correlation. The local minima and universality problems of the standard NMF can be significantly addressed by adopting the MRF model and complexity mapping technique. However
the precision of the proposed algorithm can deteriorate as the correlation feature of hyperspectral data becomes increasingly vague. If the spatial and spectral correlations of the data are low
then the precision of the algorithm can be reduced. The next step is to widen the application scope of the unmixing algorithm and stabilize its performance.
Bioucas J M and Nascimento J M P. 2008. Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46(8): 2435–2445
Chen B L, Li M, Wang J X and Wu F X. 2014. Disease gene identification by using graph kernels and Markov random fields. Science China Life Sciences, 57(11): 1054–1063
Chen X W, Yu J and Sun W D. 2013. Area-correlated spectral unmixing based on Bayesian nonnegative matrix factorization. Open Journal of Applied Sciences, 3(1): 41–46
Deng H W and Clausi D A. 2004. Unsupervised image segmentation using a simple MRF model with a new implementation scheme. Pattern Recognition, 37(12): 2323–2335
杜世强, 石玉清, 王维兰, 马明. 2012. 基于图正则化的半监督非负矩阵分解. 计算机工程与应用, 48(36): 194–200
Du S Q, Shi Y Q, Wang W L and Ma M. 2012. Graph regularized-based semi-supervised non-negative matrix factorization. Computer Engineering and Applications, 48(36): 194–200 (
Geng X R, Ji L Y and Sun K. 2016. Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data. Frontiers of Information Technology and Electronic Engineering, 17(5): 403–412
Hoyer P O. 2004. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5(1): 1457–1469
黄春海. 2014. 基于非负矩阵分解的高光谱图像解混技术研究. 西安: 西安电子科技大学: 36–38
Huang C H. 2014. Researches on Hyperspectral Unmixing Based on Nonnegative Matrix Factorization. Xi’an: Xidian University: 36–38
Jia S and Qian Y T. 2009. Constrained nonnegative matrix factorization for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 161–173
姜小燕, 孙福明, 李豪杰. 2016. 基于图正则化和稀疏约束的半监督非负矩阵分解. 计算机科学, 43(7): 77–82, 105
Jiang X Y, Sun F M and Li H J. 2016. Semi-supervised nonnegative matrix factorization based on graph regularization and sparseness constraints. Computer Science, 43(7): 77–82, 105 (
Lee D D and Seung H S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788–791
Liu B, Liu J Z, Zhang G L, Ling Z C, Zhang J, He Z P, Yang B Y and Zou Y L. 2014. Correlation analysis and partial least square modeling to quantify typical minerals with Chang’E-3 visible and near-infrared imaging spectrometer’s ground validation data. Chinese Journal of Geochemistry, 33(1): 86–94
刘建军, 吴泽彬, 韦志辉, 肖亮, 孙乐. 2012. 基于空间相关性约束稀疏表示的高光谱图像分类. 电子与信息学报, 34(11): 2666–2671
Liu J J, Wu Z B, Wei Z H, Xiao L and Sun L. 2012. Spatial correlation constrained sparse representation for hyperspectral image classification. Journal of Electronics and Information Technology, 34(11): 2666–2671 (
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
Pauca V P, Pipe J and Plemmons R J. 2006. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and its Applications, 416(1): 29–47
汤毅, 万建伟, 许可, 王玲. 2014. 地物空间分布特性的高光谱遥感图像解混算法. 红外与毫米波学报, 33(5): 560–570
Tang Y, Wan J W, Xu K and Wang L. 2014. Hyperspectral unmixing based on material spatial distribution characteristic. Journal of Infrared and Millimeter Waves, 33(5): 560–570 (
王楠, 张良培, 杜博. 2014. 最小光谱相关约束NMF的高光谱遥感图像混合像元分解. 武汉大学学报(信息科学版), 39(1): 22–26
Wang N, Zhang L P and Du B. 2014. Minimum spectral correlation constraint algorithm based on non-negative matrix factorization for hyperspectral unmixing. Geomatics and Information Science of Wuhan University, 39(1): 22–26 (
吴金彪. 2002. D-N交替迭代法及其收敛性分析. 数值计算与计算机应用, 23(2): 121–130
Wu J B. 2002. D-N commutative method and its convergence. Journal on Numerical Methods and Computer Applications, 23(2): 121–130 (
许宁, 尤红建, 耿修瑞, 曹银贵. 2016. 基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法. 电子与信息学报, 38(11): 2701–2708
Xu N, You H J, Geng X R and Cao Y G. 2016. Multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery. Journal of Electronics and Information Technology, 38(11): 2701–2708 (
Xu X R, Chen L F and Zhuang J L. 2001. Genetic inverse algorithm for retrieval of component temperature of mixed pixel by multi-angle thermal infrared remote sensing data. Science in China Series D: Earth Sciences, 44(4): 363–372
尤传雨, 刘文波, 常军. 2016. 基于复杂追踪理论的结构模态参数识别. 苏州科技学院学报(工程技术版), 29(2): 38–42
You C Y, Liu W B and Chang J. 2016. Structural modal parameter identification based on complexity pursuit. Journal of Suzhou University of Science and Technology (Engineering and Technology), 29(2): 38–42 (
詹锡兰, 吴波. 2011. 一种基于高斯马尔可夫随机场模型的混合像元分解方法. 福州大学学报(自然科学版), 39(1): 60–66
Zhan X L and Wu B. 2011. A method of spectral mixture analysis based on Gaussian Markov random field model. Journal of Fuzhou University (Natural Science Edition), 39(1): 60–66 (
Zhao C H, Zhu H F, Cui S L and Qi B. 2015. Multiple endmember hyperspectral sparse unmixing based on improved OMP algorithm. Journal of Harbin Institute of Technology, 22(5): 97–104
Zymnis A, Kim S J, Skaf J, Parente M and Boyd S. 2007. Hyperspectral image unmixing via alternating projected subgradients. Proceedings of 2007 Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA: IEEE: 598–610 [DOI: 10.1109/ACSSC.2007.4487406]
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