一种基于谱聚类算法的高光谱遥感图像分类方法
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摘要
结合K-means算法和谱聚类方法的优点,提出一种新的高光谱图像聚类方法。该方法在对高光谱图像数据进行特征降维的基础上,采用K-means算法对图像进行粗聚类处理,然后采用谱聚类方法对粗聚类结果进行较高精度的聚类。与K-means聚类算法相比,该方法有效提高了高光谱图像聚类的分类精度。对模拟数据和真实的高光谱数据的对比实验表明,相对于K-means和谱聚类方法,该方法具有良好的聚类性能。
As common unsupervised clustering methods, K-means and spectral clustering methods have some disadvantages and limitations in clustering hyperspectral remote sensing image. Aiming at these problems, a new clustering method of hyperspectral image is proposed in this study. In this method, based on the feature reduction dimension of hyperspectral image data, K-means algorithm is first used to make rough clustering of images. Then spectral clustering method is used to cluster the results of coarse clustering with high precision. Compared with K-means clustering algorithm, this method effectively improves the classification accuracy of hyperspectral image clustering. Experiments on simulated data and real hyperspectral data show that this method has good clustering performance compared with K-means and spectral clustering methods.
As common unsupervised clustering methods, K-means and spectral clustering methods have some disadvantages and limitations in clustering hyperspectral remote sensing image. Aiming at these problems, a new clustering method of hyperspectral image is proposed in this study. In this method, based on the feature reduction dimension of hyperspectral image data, K-means algorithm is first used to make rough clustering of images. Then spectral clustering method is used to cluster the results of coarse clustering with high precision. Compared with K-means clustering algorithm, this method effectively improves the classification accuracy of hyperspectral image clustering. Experiments on simulated data and real hyperspectral data show that this method has good clustering performance compared with K-means and spectral clustering methods.
引文
[1] 童庆禧. 高光谱遥感[M]. 北京: 高等教育出版社, 2006: 47-65.
[2] 万余庆. 高光谱遥感应用研究[M]. 北京: 科学出版社, 2006: 35-98.
[3] Ma X L, Ren Z Y, Wang Y L. Research on hyperspectral remote sensing image classification based on SAM[J].System Sciences & Comprehensive Studies in Agriculture, 2009, 25(2):204-208.
[4] Shafri H Z M, Suhaili A, Mansor S. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis[J]. Journal of Computer Science, 2007, 3(6):419-423.
[5] Gao L, Li J, Khodadadzadeh M, et al. Subspace-based support vector machines for hyperspectral image classifica- tion[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(2):349-353.
[6] Hu W, Huang Y, Wei L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015(2):1-12.
[7] Han J, Kamber M. Data mining concepts and technique[M]. San Fransisco: Morgan Kaufmann press, 2001:23-106.
[8] Macqueen J. Some methods for classification and analysis of MultiVariate observations[C]//Proc of Berkeley Symposium on Mathematical Statistic- sand Probability, 1967:281-297.
[9] Wagstaff K, Cardie C, Rogers S, et al. Constrained K-means clustering with background knowledge[C]//Eighteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Press, 2001:577-584.
[10] Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(5):603-619.
[11] Ball G H, Hall D J. ISODATA, a novel method of data analysis and pattern classification[R]. Stanford research inst Menlo Park CA, 1965.
[12] Ester M, Kriegel H P, Xu X. A densitybased algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996:226-231.
[13] 蔡晓妍, 戴冠中, 杨黎斌. 谱聚类算法综述[J]. 计算机科学, 2008, 35(7):14-18.
[14] Ng A Y, Jordan M I, Weiss Y. On spectral clustering: analysis and an algorithm[J]. Proc Nips, 2001, 14:849-856.
[15] Zelnik-Manor L. Self-tuning spectral clustering[J]. Advances in Neural Information Processing Systems, 2004, 17:1 601-1 608.
[16] Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering[J].IEEE Transactions on Com-puter-Aided Design of Integrated Circuits and Systems, 1992, 11(9):1 074-1 085.
[17] Abdi H, Williams L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews Computational Statistics, 2010, 2(4):433-459.
[18] Green A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J]. IEEE Transactions on Geoscience & Remote Sensing, 1988, 26(1):65-74.
[19] Chen L J, Zou X J, Chen B B, et al. An improved FastICA algorithm and its application in image feature extraction[J]. Advanced Materials Research, 2011, 204-210:1 485-1 489.
[20] Pudn.com.Clustering datasets:China [EB/OL].(2015-04-18)[2018-01-03]. http://www.pu dn.com/Download/item/id/2741142.html.
[21] Tsaparas P, Mannila H, Gionis A. Clustering aggregation[J]. Acm Transactions on Knowledge Discovery from Data, 2007, 1(1):4.
[22] 李翔. 高光谱影像的聚类分析及应用[D]. 北京:北京交通大学, 2015.
[2] 万余庆. 高光谱遥感应用研究[M]. 北京: 科学出版社, 2006: 35-98.
[3] Ma X L, Ren Z Y, Wang Y L. Research on hyperspectral remote sensing image classification based on SAM[J].System Sciences & Comprehensive Studies in Agriculture, 2009, 25(2):204-208.
[4] Shafri H Z M, Suhaili A, Mansor S. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis[J]. Journal of Computer Science, 2007, 3(6):419-423.
[5] Gao L, Li J, Khodadadzadeh M, et al. Subspace-based support vector machines for hyperspectral image classifica- tion[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(2):349-353.
[6] Hu W, Huang Y, Wei L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015(2):1-12.
[7] Han J, Kamber M. Data mining concepts and technique[M]. San Fransisco: Morgan Kaufmann press, 2001:23-106.
[8] Macqueen J. Some methods for classification and analysis of MultiVariate observations[C]//Proc of Berkeley Symposium on Mathematical Statistic- sand Probability, 1967:281-297.
[9] Wagstaff K, Cardie C, Rogers S, et al. Constrained K-means clustering with background knowledge[C]//Eighteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Press, 2001:577-584.
[10] Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(5):603-619.
[11] Ball G H, Hall D J. ISODATA, a novel method of data analysis and pattern classification[R]. Stanford research inst Menlo Park CA, 1965.
[12] Ester M, Kriegel H P, Xu X. A densitybased algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996:226-231.
[13] 蔡晓妍, 戴冠中, 杨黎斌. 谱聚类算法综述[J]. 计算机科学, 2008, 35(7):14-18.
[14] Ng A Y, Jordan M I, Weiss Y. On spectral clustering: analysis and an algorithm[J]. Proc Nips, 2001, 14:849-856.
[15] Zelnik-Manor L. Self-tuning spectral clustering[J]. Advances in Neural Information Processing Systems, 2004, 17:1 601-1 608.
[16] Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering[J].IEEE Transactions on Com-puter-Aided Design of Integrated Circuits and Systems, 1992, 11(9):1 074-1 085.
[17] Abdi H, Williams L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews Computational Statistics, 2010, 2(4):433-459.
[18] Green A, Berman M, Switzer P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J]. IEEE Transactions on Geoscience & Remote Sensing, 1988, 26(1):65-74.
[19] Chen L J, Zou X J, Chen B B, et al. An improved FastICA algorithm and its application in image feature extraction[J]. Advanced Materials Research, 2011, 204-210:1 485-1 489.
[20] Pudn.com.Clustering datasets:China [EB/OL].(2015-04-18)[2018-01-03]. http://www.pu dn.com/Download/item/id/2741142.html.
[21] Tsaparas P, Mannila H, Gionis A. Clustering aggregation[J]. Acm Transactions on Knowledge Discovery from Data, 2007, 1(1):4.
[22] 李翔. 高光谱影像的聚类分析及应用[D]. 北京:北京交通大学, 2015.