A method of hyperspectral remote sensing image classification based on spectral clustering

doi:10.7523/j.issn.2095-6134.2019.02.015

Abstract

Abstract: 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.

Key words: hyperspectral images, clustering, spectral clustering, K-means

CLC Number:

TP391

YANG Suixin, GENG Xiurui, YANG Weitun, ZHAO Yongchao, LU Xiaojun. A method of hyperspectral remote sensing image classification based on spectral clustering[J]. , 2019, 36(2): 267-274.

References

[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, 17:1601-1608.
[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):1074-1085.
[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, 204-210:1485-1489.
[20] Pudn.com.Clustering datasets:China[EB/OL].(2015-04-18)[2018-01-03]. http://www.pudn.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.