浏览全部资源
扫码关注微信
纸质出版日期： 2022-02-07 ，
扫 描 看 全 文
孙伟伟，杨刚，彭江涛，孟祥超.2022.鲁棒多特征谱聚类的高光谱影像波段选择.遥感学报，26（2）： 397-405
Sun W W，Yang G，Peng J T and Meng X C. 2022. Robust multi-feature spectral clustering for hyperspectral band selection. National Remote Sensing Bulletin， 26（2）：397-405
传统谱聚类的高光谱影像波段选择模型中，采用的波段相似矩阵受到噪声或异常值的影响且仅能表征波段的单一相似特征，导致波段子集的选取结果受到限制。本文从波段选择的目的出发，提出鲁棒多特征谱聚类方法，整合多个特征的波段相似矩阵来形成综合相似矩阵以解决上述问题。该方法假设4种相似性度量包括光谱信息散度、光谱角度距离、波段相关性和拉普拉斯图谱能够共同揭示波段聚类的内在结构特征，通过构建低秩稀疏矩阵分解模型来表征单一相似矩阵与综合相似矩阵的内在关系。进一步，采用增强拉格朗日乘子算法来优化求解综合相似矩阵，利用常规谱聚类方法来聚合所有波段至不同的类别，并选取代表性波段。采用两个常用的高光谱影像数据，对比5种常用的波段选择方法来进行实验验证。实验结果表明，鲁棒多特征谱聚类方法优于改进稀疏子空间聚类、常规谱聚类方法和其他主流波段选择方法，而且计算效率较高。
The Hughes problem together with strong intra-band correlations and massive data seriously hinders hyperspectral processing and further applications. Dimensionality reduction using band selection can be used to conquer the abovementioned problems and guarantee the application performance of hyperspectral data. In particular
spectral clustering is a typical method for high-dimensional hyperspectral data. This method finds clusters of all hyperspectral bands on the connected graph and selects the representatives. Unfortunately
the regular similarity measures are negatively affected by outliers or noise of hyperspectral data in measuring the similarity of different bands. They could also only represent one feature of band similarity and have respective limitations. Accordingly
the obtained similarity matrix could not represent the full information of band selection required and could not guarantee obtaining aimed bands from spectral clustering. Therefore
we propose a Robust Multifeature Spectral Clustering (RMSC) method to solve the two problems mentioned above and enhance the performance of hyperspectral band selection from spectral clustering.
The RMSC combines multiple features of similarity measures for pairwise bands
namely
information entropy
band correlation
and band dissimilarity
to construct the integrated similarity matrix. It utilizes spectral information divergence to quantify the information entropy between pairwise bands. The coefficient correlation is utilized to measure the band correlations and construct the similarity matrix of band correlations. The Laplacian graph is also adopted to construct a similarity matrix and show the dissimilarity between different bands considering the inner clustering structure of all bands. The spectral angle distance matrix is constructed as well to reflect the similarity from the aspects of overall differences. The RMSC regards that each similarity matrix of all four features reflect the underlying true clustering information of all bands and has low-rank property. It formulates the estimation of combined dissimilarity matrix into a low-rank and sparse decomposition problem and utilizes the augmented Lagrangian multiplier to solve it. Thereafter
it implements the regular spectral clustering on the integrated similarity matrix and selects the representative bands from each cluster.
Two hyperspectral datasets are used to design four groups of experiments and testify the performance of RMSC. Five state-of-the-art methods
namely
WaluDI
fast density-peak-based clustering
orthogonal projections based band selection
Improved Sparse Spectral Clustering (ISSC) and SC-SID
and support vector machine
are used to quantify the classification accuracy. Experimental results show that RSMC outperforms the five other band selection methods in overall classification accuracy with shorter computational time. The regularization parameter is insensitive to RMSC
and a small candidate could produce high classification accuracy.
RMSC is better in selecting representative bands than current spectral clustering such as ISSC. It can also be a good choice in hyperspectral dimensionality reduction.
遥感高光谱遥感降维波段选择分类鲁棒多特征谱聚类
remote sensinghyperspectral remote sensingdimensionality reductionband selectionspectral clusteringrobust multi-feature spectral clustering
Chang C I, Du Q, Sun T L and Althouse M L G. 1999. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 37(6): 2631-2641 [DOI: 10.1109/36.803411http://dx.doi.org/10.1109/36.803411]
Chang C I and Wang S. 2006. Constrained band selection for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1575-1585 [DOI: 10.1109/TGRS.2006.864389http://dx.doi.org/10.1109/TGRS.2006.864389]
Du Q and Yang H. 2008. Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 5(4): 564-568 [DOI: 10.1109/LGRS.2008.2000619http://dx.doi.org/10.1109/LGRS.2008.2000619]
Jia S, Tang G, Zhu J S and Li Q Q. 2016. A novel ranking-based clustering approach for hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing, 54(1): 88-102 [DOI: 10.1109/TGRS.2015.2450759http://dx.doi.org/10.1109/TGRS.2015.2450759]
Lin Z C, Chen M M, Wu L Q and Ma Y. 2015. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-rank Matrices. UILU-ENG-09-2215. Coordinated Science Laboratory, University of Illinois at Urbana-Champaign
Martínez-Usómartinez-Uso A, Pla F, Sotoca J M and GarcÍa-Sevilla P. 2007. Clustering-based hyperspectral band selection using information measures. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 4158-4171 [DOI: 10.1109/TGRS.2007.904951http://dx.doi.org/10.1109/TGRS.2007.904951]
Pan B, Shi Z Z and Xu X. 2019. Analysis for the weakly Pareto optimum in multiobjective-based hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 3729-3740 [DOI: 10.1109/TGRS.2018.2886853http://dx.doi.org/10.1109/TGRS.2018.2886853]
Sun W W and Du Q. 2018. Graph-regularized fast and robust principal component analysis for hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing, 56(6): 3185-3195 [DOI: 10.1109/TGRS.2018.2794443http://dx.doi.org/10.1109/TGRS.2018.2794443]
Sun W W, Jiang M and Li W Y. 2017. Band selection using sparse self-representation for hyperspectral imagery. Geomatics and Information Science of Wuhan University, 42(4): 441-448
孙伟伟, 蒋曼, 李巍岳. 2017. 利用稀疏自表达实现高光谱影像波段选择. 武汉大学学报(信息科学版), 42(4): 441-448 [DOI: 10.13203/j.whugis20150052http://dx.doi.org/10.13203/j.whugis20150052]
Sun W W, Li W Y, Li J and Lai Y M. 2015a. Band selection using sparse nonnegative matrix factorization with the thresholded Earth's mover distance for hyperspectral imagery classification. Earth Science Informatics, 8(4): 907-918 [DOI: 10.1007/s12145-014-0201-3http://dx.doi.org/10.1007/s12145-014-0201-3]
Sun W W, Tian L, Xu Y, Zhang D F and Du Q. 2017a. Fast and robust self-representation method for hyperspectral band selection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11): 5087-5098 [DOI: 10.1109/JSTARS.2017.2737400http://dx.doi.org/10.1109/JSTARS.2017.2737400]
Sun W W, Yang G, Du B, Zhang F F and Zhang L P. 2017b. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 4032-4046 [DOI: 10.1109/TGRS.2017.2686842http://dx.doi.org/10.1109/TGRS.2017.2686842]
Sun W W, Zhang D F, Yang G and Li W Y. 2018. Band selection for hyperspectral imagery based on weighted probabilistic archetypal analysis. Journal of Remote Sensing, 22(1): 110-118
孙伟伟, 张殿发, 杨刚, 李巍岳. 2018. 加权概率原型分析的高光谱影像波段选择. 遥感学报, 22(1): 110-118 [DOI: 10.11834/jrs.20186446http://dx.doi.org/10.11834/jrs.20186446]
Sun W W, Zhang L P, Du B, Li W Y and Lai Y M. 2015b. Band selection using improved sparse subspace clustering for hyperspectral imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2784-2797 [DOI: 10.1109/JSTARS.2015.2417156http://dx.doi.org/10.1109/JSTARS.2015.2417156]
Sun W W, Zhang L P, Zhang L F and Lai Y M. 2016. A dissimilarity-weighted sparse self-representation method for band selection in hyperspectral imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9): 4374-4388 [DOI: 10.1109/JSTARS.2016.2539981http://dx.doi.org/10.1109/JSTARS.2016.2539981]
Tong Q X, Xue Y Q and Zhang L F. 2014. Progress in hyperspectral remote sensing science and technology in China over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 70-91 [DOI: 10.1109/JSTARS.2013.2267204http://dx.doi.org/10.1109/JSTARS.2013.2267204]
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.20166264http://dx.doi.org/10.11834/jrs.20166264]
von Luxburg U. 2007. A tutorial on spectral clustering. Statistics and Computing, 17(4): 395-416 [DOI: 10.1007/s11222-007-9033-zhttp://dx.doi.org/10.1007/s11222-007-9033-z]
Wang Q, Zhang F H and Li X L. 2018. Optimal clustering framework for hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing, 56(10): 5910-5922 [DOI: 10.1109/TGRS.2018.2828161http://dx.doi.org/10.1109/TGRS.2018.2828161]
Xia R K, Pan Y, Du L and Yin J. 2014. Robust multi-view spectral clustering via low-rank and sparse decomposition//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: AAAI: 2149-2155
Yuan Y, Lin J Z and Wang Q. 2016. Dual-clustering-based hyperspectral band selection by contextual analysis. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1431-1445 [DOI: 10.1109/TGRS.2015.2480866http://dx.doi.org/10.1109/TGRS.2015.2480866]
Zhang B. 2016. Advancement of hyperspectral image processing 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.20166179http://dx.doi.org/10.11834/jrs.20166179]
Zhang B, Li S S, Jia X P, Gao L R and Peng M. 2011. Adaptive Markov random field approach for classification of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 8(5): 973-977 [DOI: 10.1109/LGRS.2011.2145353http://dx.doi.org/10.1109/LGRS.2011.2145353]
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.20166050http://dx.doi.org/10.11834/jrs.20166050]
Zhang L P, Liu R and Du B. 2018. Hyperspectral remote sensing image processing by using quantum optimization algorithm. Geomatics and Information Science of Wuhan University, 43(12): 1811-1818
张良培, 刘蓉, 杜博. 2018. 使用量子优化算法进行高光谱遥感影像处理综述. 武汉大学学报(信息科学版), 43(12): 1811-1818 [DOI: 10.13203/j.whugis20180231http://dx.doi.org/10.13203/j.whugis20180231]
Zhang W Q, Li X Y, Dou Y X and Zhao L Y. 2018. A geometry-based band selection approach for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4318-4333 [DOI: 10.1109/TGRS.2018.2811046http://dx.doi.org/10.1109/TGRS.2018.2811046]
Zhong Y F, Wang X Y, Xu Y, Wang S Y, Jia T Y, Hu X, Zhao J, Wei L F and Zhang L P. 2018. Mini-UAV-borne hyperspectral remote sensing: from observation and processing to applications. IEEE Geoscience and Remote Sensing Magazine, 6(4): 46-62 [DOI: 10.1109/MGRS.2018.2867592http://dx.doi.org/10.1109/MGRS.2018.2867592]
相关作者
相关机构
京公网安备11010802024621