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DOI: 10.18287/2412-6179-2017-41-4-564-572

Страницы: 564-572.

Abstract:
Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.

Keywords:
hyperspectral image, segmentation, clustering, watershed transform, region growing, region merging, segmentation quality measure, global consistency error, rand index.

Citation:
Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412-6179-2017-41-4-564-572.

1. Fu KS, Mui JK. A survey on image segmentation. Pattern Recognition 1981; 13(1): 3-16. DOI: 10.1016/0031-3203(81)90028-5.
2. Berthier M, El Asmar S, Frélicot C. Binary codes K-modes clustering for HSI segmentation. 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) 2016; 1-5. DOI: 10.1109/IVMSPW.2016.7528190.
3. Cariou C, Chehdi K. Unsupervised nearest neighbors clustering with application to hyperspectral images. IEEE Journal of Selected Topics in Signal Processing 2015; 9(6): 1105-1116. DOI: 10.1109/JSTSP.2015.2413371.
4. Noyel G, Angulo J, Jeulin D. Morphological segmentation of hyperspectral images. Image Analysis and Stereology 2007; 26(3): 101-109. DOI: 10.5566/ias.v26.p101-109.
5. Tarabalka Y, Chanussot J, Benediktsson JA. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition 2010; 43(7): 2367-2379. DOI: 10.1016/j.patcog.2010.01.016.
6. Goretta N, Rabatel G, Fiorio C, Lelong C, Roger JM. An iterative hyperspectral image segmentation method using a cross analysis of spectral and spatial information. Chemometrics and Intelligent Laboratory Systems 2012; 117(1): 213-223. DOI: 10.1016/j.chemolab.2012.05.004.
7. Kuznetsov AV, Myasnikov VV. A comparison of algorithms for supervised classification using hyperspectral data. Computer Optics 2014; 38(3): 494-502.
8. Denisova AYu, Myasnikov VV. Anomaly detection for hyperspectral imaginary. Computer Optics 2014; 38(2): 287-296.
9. Richards JA, Jia X, Ricken DE, Gessner W. Remote sensing digital image analysis: An introduction. New York: Springer-Verlag Inc; 1999. ISBN: 978-3-540-64860-7.
10. Wang J, Chang C-I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans Geosci Remote Sens 2006; 44(6): 1586-1600. DOI: 10.1109/TGRS.2005.863297.
11. Myasnikov EV. Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis. Proc SPIE 2015; 9875: 987508. DOI: 10.1117/12.2228831.
12. Myasnikov E. Evaluation of stochastic gradient descent methods for nonlinear mapping of hyperspectral data. In book: Campilho A, Karray F, eds. ICIAR 2016, LNCS 2016; 9730: 276-283. DOI: 10.1007/978-3-319-41501-7_31.
13. Sun W, Halevy A, Benedetto JJ, Czaja W, Liu C, Wu H, Shi B, Li W. UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification. ISPRS Journal of Photogrammetry and Remote Sensing 2014; 89: 25-36. DOI: 10.1016/j.isprsjprs.2013.12.003.
14. Kim DH, Finkel LH. Hyperspectral image processing using locally linear embedding. First International IEEE EMBS Conference on Neural Engineering 2003; 316-319. DOI: 10.1109/CNE.2003.1196824.
15. Doster T, Olson CC. Building robust neighborhoods for manifold learning-based image classification and anomaly detection. Proc SPIE 2016; 9840: 984015. DOI: 10.1117/12.2227224.
16. Lloyd SP. Least squares quantization in PCM. IEEE Transactions on Information Theory 1982; 28(2): 129-137. DOI: 10.1109/TIT.1982.1056489.
17. Arthur D, Vassilvitskii S. K-means++: The advantages of careful seeding. SODA'07 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms 2007; 1027-1035. DOI: 10.1145/1283383.1283494.
18. Beucher S, Lantuejoul C. Use of watersheds in contour detection. International Workshop Image Processing, Real-Time Edge and Motion Detection/Estimation 1979.
19. Zimichev EA, Kazanskiy NL, Serafimovich PG. Spectral-spatial classification with k-means++ particional clustering. Computer Optics 2014; 38(2): 281-286.
20. Huang Q, Dom B. Quantitative methods of evaluating image segmentation. Proceedings of IEEE International Conference on Image Processing 1995; 3: 3053-3056. DOI: 10.1109/ICIP.1995.537578.
21. Martin D, Fowlkes C, Tal D, Malik J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of Eighth IEEE International Conference on Computer Vision 2001; II: 416-423. DOI: 10.1109/ICCV.2001.937655.
22. Monteiro FC, Campilho AC. Performance evaluation of image segmentation. In book: Campilho A, Kamel MS, eds. ICIAR 2006, LNCS 2006; 4141: 248-259. DOI: 10.1007/11867586_24.
23. Unnikrishnan RA, Pantofaru C, Hebert M. Measure for objective evaluation of image segmentation algorithms. CVPR Workshops 2005; 34. DOI: 10.1109/CVPR.2005.390.
24. Rand WM. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 1971; 66(336): 846-850. DOI: 10.2307/2284239.
25. Monteiro FC, Campilho AC. Distance measures for image segmentation evaluation. Numerical Analysis and Applied Mathematics ICNAAM 2012, AIP Conference Proceedings 2012; 1479: 794-797. DOI: 10.1063/1.4756257.
26. Meila M. Comparing clusterings by the variation of information. In book: Schölkopf B, Warmuth MK, eds. Learning Theory and Kernel Machines. LNCS 2003; 2777. DOI: 10.1007/978-3-540-45167-9_14.
27. Hyperspectral Remote Sensing Scenes. Source: <http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes>.