An unsupervised method of classifying remotely sensed images using Kohonen self-organizing maps and agglomerative hierarchical clustering methods

International Journal of Remote Sensing

Volumn 29, Issue 11, 2008, Pages 3171-3207

  Goncalves M L ^a   Netto, M L A ^b   Costa, J A F ^c   Zullo Junior, J ^b  

^a PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

^c FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PROBABILITY DISTRIBUTIONS; REMOTE SENSING; SELF ORGANIZING MAPS;

AGGLOMERATIVE HIERARCHICAL CLUSTERING; AUTOMATIC CLASSIFICATION; HIERARCHICAL CLUSTERING METHODS; KOHONEN SELF-ORGANIZING MAPS; PARTITIONAL CLUSTERING; REMOTELY SENSED IMAGES; RESTRICTED CONNECTIVITY; UNSUPERVISED CLASSIFICATION;

CLUSTER ANALYSIS;

ALGORITHM; CLUSTER ANALYSIS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MAP; PROBABILITY; REMOTE SENSING;

EID: 46249084070     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160701442146     Document Type: Article

References (99)

1. Arai, K. (1992) A supervised thematic mapper classification with a purification of training samples. International Journal of Remote Sensing, 13, pp. 2039-2049.
2. Atkinson, P. M. and Tatnall, A. R. L. (1997) Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18, pp. 699-709.
3. Azcarraga, A. P. (2000) Assessing self-organization using order metrics. Como, Italy Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, 6, pp. 159-164.
4. Bachmann, C. M. (2003) Improving the performance of classifiers in high-dimensional remote sensing applications: An adaptive resampling strategy for error-prone exemplars (ARESEPE). IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 2101-2112.
5. Ball, G. and Hall, D. (1967) A clustering technique for summarizing multivariate data. Behavior Science, 12, pp. 153-155.
6. Bandyopadhyay, S. (2005) Satellite image classification using genetically guided fuzzy clustering with spatial information. International Journal of Remote Sensing, 26, pp. 579-593.
7. Baraldi, A., Blonda, P., Parmiggiani, F. and Satalino, G. (2000) Contextual clustering for image segmentation. Optical Engineering, 39, pp. 1-17.
8. Bezdek, J. C. and Pal, N. R. (1998) Some new indexes of cluster validity. IEEE Transactions on Systems Man and Cybernetics, 28, pp. 301-315.
9. Bo, Y., Wen-Qi, D. and Zhong, C. (2006) New method to estimate scaling exponents of power-law degree distribution and hierarchical clustering function for complex networks. Applied Mathematics and Mechanics, 27, pp. 1475-1479.
10. Cassano, E. N., Lynch, A. H., Cassano, J. J. and Koslow, M. R. (2006) Classification of synoptic patterns in the western Arctic associated with extreme events at Barrow, Alaska, USA. Climate Research, 30, pp. 83-97.
11. Chen, D. and Stow, D. (2002) The effect of training strategies on supervised classification at different spatial resolutions. Photogrammetric Engineering and Remote Sensing, 68, pp. 1155-1162.
12. Chung, K. and Lin, J. (2007) Faster and more robust point symmetry-based K-means algorithm. Pattern Recognition, 40, pp. 410-422.
13. Claussen, J. C. and Schuster, H. G. (2002) Asymptotic level density of the elastic net self-organizing feature map. Lecture Notes in Computer Science, 2415, pp. 939-944.
14. Cohn, D., Atlas, L. and Ladner, R. (1994) Improving generalization with active learning. Machine Learning, 15, pp. 201-221.
15. Coleou, T., Poupon, M. and Azbel, K. (2003) Unsupervised seismic facies classification: A review and comparison of techniques and implementation. The Leading Edge, 22, pp. 942-953.
16. Costa, J. A. F. and Netto, M. L. A. (2001) Clustering of complex shaped data sets via Kohonen maps and mathematical morphology. Orlando, FL Proceedings of the SPIE Conference on Data Mining and Knowledge Discovery, 4384, pp. 16-27.
17. Dean, A. M. and Smith, G. M. (2003) An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing, 24, pp. 2905-2920.
18. Duda, T. and Canty, M. (2002) Unsupervised classification of satellite imagery: Choosing a good algorithm. International Journal of Remote Sensing, 23, pp. 2193-2212.
19. Espinosa, C. H., Redondo, M. F. and Sospedra, J. (2004) Some experiments on ensembles of neural networks for hyperspectral image classification. Lecture Notes in Computer Science, 3213, pp. 677-684.
20. Faloutsos, M., Faloutsos, P. and Faloutos, C. (1999) On power-law relationships of the Internet topology. ACM SIGCOMM Computer Communications Review, 29, pp. 251-262.
21. Fayos, J. and Fayos, C. (2007) 2, p. e210.
22. Foody, G. M. (1999) The significance of border training patterns in classification by a feedforward neural network using back propagation learning. International Journal of Remote Sensing, 20, pp. 3549-3562.
23. Foody, G. M. and Mathur, A. (2004) Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification. Remote Sensing of Environment, 93, pp. 107-117.
24. Frizzelle, B. G. and Moody, A. (2001) Mapping continuous distributions of land cover: A comparison of maximum-likelihood estimation and artificial neural networks. Photogrammetric Engineering and Remote Sensing, 67, pp. 693-705.
25. Giacinto, G., Roli, F. and Fumera, G. (2000) Unsupervised learning of neural network ensembles for image classification. Como, Italy Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, 3, pp. 155-159.
26. GonCalves, M. L. (1998) A neural system for the patterns recognition of remote sensing images. Kitakyushu, Japan Proceedings of The Fifth International Conference on Neural Information Processing (ICONIP'98), 2, pp. 360-364.
27. GonCalves, M. L., Netto, M. L. A. and Zullo Junior, J. (1996) A neural architecture for image classification using kohonen feature extraction. Vienna, Austria Proceedings of XVIII International Congress for Photogrammetry and Remote Sensing (ISPRS, Commission II), 31, pp. 117-122.
28. GonCalves, M. L., Netto, M. L. A., Costa, J. A. F. and Zullo Junior, J. (2005) Automatic remotely sensed data clustering by tree-structured self-organizing maps. Seoul, Korea Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05), 1, p. 4.
29. GonCalves, M. L., Netto, M. L. A., Costa, J. A. F. and Zullo Junior, J. (2006) Data clustering using self-organizing maps segmented by mathematic morphology and simplified cluster validity indexes. Vancouver, Canada Proceedings of IEEE International Joint Conference on Neural Networks, 1, pp. 8854-8861.
30. Hadjimitsis, D. G., Evangelou, I., Lazakidou, A. and Clayton, C. R. I. (2003) Unsupervised classification of remotely sensed images using self-organizing maps for mapping land-cover changes. Nottingham, UK Proceedings of the Remote Sensing and Photogrammetry Society Annual Conference
31. Hadzikadic, M., Qin, Z. and Sun, M. (2005) Network classification. Journal of Experimental and Theoretical Artificial Intelligence, 17, pp. 35-44.
32. Halkidi, M. and Vazirgiannis, M. (2002) Clustering validity assessment using multi representatives. Thessaloniki, Greece Proceedings of SETN Conference
33. Hansen, M., Dubayah, R. and Defries, R. (1996) Classification trees: An alternative to traditional land cover classifiers. International Journal of Remote Sensing, 17, pp. 1075-1081.
34. Haykin, S. (1994) Neural Networks: A Comprehensive Foundation, Prentice-Hall, Englewood Cliffs, NJ
35. Hong, Y., Hsu, K. L., Sorooshian, S. and Gao, X. G. (2005) Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation from satellite imagery at small scales. Water Resources Research, 41
36. Hong, Y., Chiang, Y., Liu, Y., Hsu, K. L. and Sorooshian, S. (2006) Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing feature mapping techniques. International Journal of Remote Sensing, 27, pp. 5165-5184.
37. Huang, K. (2002) The use of a newly developed algorithm of divisive hierarchical clustering for remote sensing image analysis. International Journal of Remote Sensing, 23, pp. 3149-3168.
38. Huang, J. Z., Ng, M. K., Rong, H. and Li, Z. (2005) Automated variable weighting in K-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, pp. 657-668.
39. Ji, C. Y. (2000) Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogrammetric Engineering and Remote Sensing, 66, pp. 1451-1460.
40. Ji, M. (2003) Using fuzzy sets to improve cluster labeling in unsupervised classification. International Journal of Remote Sensing, 24, pp. 657-671.
41. Kaski, S. and Lagus, K. (1996) Comparing self-organizing maps. Proceedings of International Conference on Artificial Neural Networks (ICANN'96), Lectures Notes in Computer Science, 1112, pp. 809-814.
42. Kelly, M., Shaari, D., Guo, Q. and Liu, D. (2004) A comparison of standard and hybrid classifier methods for mapping hardwood mortality in areas affected by 'sudden oak death'. Photogrammetric Engineering and Remote Sensing, 70, pp. 1229-1239.
43. Khan, S. S. and Ahmad, A. (2004) Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters, 25, pp. 1293-1302.
44. Kiang, M. Y. (2001) Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics and Data Analysis, 38, pp. 161-180.
45. Kohonen, T. (1997) Self-Organizing Maps, Springer-Verlag, Berlin
46. Kohonen, T., Kaski, S. and Lappalainen, H. (1997) Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM. Neural Computation, 9, pp. 1321-1344.
47. Lance, G. N. and Willians, W. T. A. (1967) General theory of classificatory sorting strategies: 1. Hierarchical Systems. Computer Journal, 9, pp. 373-380.
48. Lawrence, R., Bunn, A., Powell, S. and Zambom, M. (2004) Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of Enviroment, 90, pp. 331-336.
49. Likas, A., Vlassis, N. and Verbeek, J. (2003) The global K-means clustering algorithm. Pattern Recognition, 36, pp. 451-461.
50. Liljeros, F., Edling, C. R. and Amaral, L. A. N. (2001) The web of human sexual contacts. Nature, 411, pp. 907-908.
51. Lillesand, T. M. and Kiefer, R. W. (2000) Remote Sensing and Image Interpretation, John Wiley & Sons Inc, New York
52. Liu, X., Skidmore, A. K. and van Oosten, H. (2002) Integration of classification methods for improvement of land-cover map accuracy. Journal of Photogrammetry and Remote Sensing, 56, pp. 257-268.
53. Liu, X., Skidmore, A. K. and van Oosten, H. (2003) An experimental study on spectral discrimination capability of a backpropagation neural network classifier. International Journal of Remote Sensing, 24, pp. 673-688.
54. Liu, A., Bicheng, L., Chen, G. and Zhangiu, X. (2005) A new ART neural networks for remote sensing image classification. Lecture Notes in Computer Science, 3611, pp. 37-42.
55. Lo, C. P. and Choi, J. (2004) A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. International Journal of Remote Sensing, 25, pp. 2687-2700.
56. Magnussen, S., Boudewyn, P. and Wulder, M. (2004) Contextual classification of Landsat TM images to forest inventory cover types. International Journal of Remote Sensing, 25, pp. 2421-2440.
57. MarCal, A. R. S. and Borges, J. S. (2005) Estimation of the 'natural' number of classes of a multispectral image. Seoul, Korea Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05), 6, pp. 3788-3791.
58. MarCal, A. R. S. and Castro, L. (2005) Hierarchical clustering of multispectral images using combined spectral and spatial criteria. IEEE Geoscience and Remote Sensing Letters, 2, pp. 59-63.
59. Merkl, D. and Rauber, A. (1997) Alternative ways for cluster visualization in self-organizing maps. Proceedings of the Workshop on Self-organizing Maps (WSOM97), pp. 106-111.
60. Milligan, G. W. and Cooper, M. C. (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, pp. 159-179.
61. Mitra, P., Shankar, U. and Pal, S. K. (2004) Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognition Letters, 25, pp. 1067-1074.
62. Pal, N. R. and Biswas, J. (1997) Cluster validation using graph theoretic concepts. Pattern Recognition, 30, pp. 847-857.
63. Pal, M. and Mather, P. M. (2002) A comparison of decision tree and backpropagation neural network classifiers for land use classification. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'02), 1, pp. 503-505.
64. Pal, S. K., Bandyopadhyay, S. and Murthy, C. A. (2001) Genetic classifiers for remotely sensed images: Comparison with standard methods. International Journal of Remote Sensing, 22, pp. 2545-2569.
65. Pal, M. and Mather, P. M. (2005) Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, pp. 1007-1011.
66. Pasquariello, G., Ancona, N., Blonda, P., Tarantino, C., Satalino, G. and D'Addabbo, A. (2002) Neural network ensemble and support vector machine classifiers for the analysis of remotely sensed data: A comparison. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'02), 1, pp. 509-511.
67. Peres, S. M. and Netto, M. L. A. (2004) A fractal fuzzy approach to clustering tendency analysis. Lecture Notes in Computer Science, 3171, pp. 395-404.
68. Richards, J. A. (2005) Analysis of remotely sensed data: The formative decades and the future. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 422-432.
69. Ritter, H. (1991) Asymptotic level density for a class of vector quantization processes. IEEE Transactions on Neural Networks, 2, pp. 173-175.
70. Sezgin, M., Ersoy, O. K. and Yazgan, B. (2004) Segmentation of remote sensing images using multistage unsupervised learning. Proceedings of SPIE, Applications of Digital Image Processing XXVII, 5558, pp. 616-623.
71. Shah, C. A., Arora, M. K. and Varshney, P. K. (2004) Unsupervised classification of hyperspectral data: An ICA misture model based approach. International Journal of Remote Sensing, 25, pp. 481-487.
72. Sorooshian, S., Hsu, K., Gao, X., Gupta, H. V., Imam, B. and Braithwaite, D. (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin American Meteorology Society, 81, pp. 2035-2046.
73. Stathakis, D. and Vasilakos, A. (2006) Comparison of computational intelligence based classification techniques for remotely sensed optical image classification. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 2305-2318.
74. Strecker, U. and Uden, R. (2002) Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps. The Leading Edge, 21, pp. 1032-1037.
75. Su, M. and Chou, C. (2001) A modified version of the K-means algorithm with a distance based on cluster symmetry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, pp. 674-680.
76. Sunar Erbek, F., Ozkan, C. and Taberner, M. (2004) Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing, 25, pp. 1733-1748.
77. Swain, P. H. and Davis, S. M. (1978) Remote Sensing: The Quantitative Approach, McGraw-Hill, New York
78. Tran, T. N., Wehrens, R. and Buydens, L. M. C. (2003) Sparef: A clustering algorithm for multispectral images. Analytica Chimica Acta, 490, pp. 303-312.
79. Tran, T. N., Wehrens, R. and Buydens, L. M. C. (2005) Clustering multispectral images: A tutorial. Chemometrics and Intelligent Laboratory Systems, 77, pp. 3-17.
80. Tso, B. and Olsen, R. C. (2005) Combining spectral and spatial information into hidden Markov models for unsupervised image classification. International Journal of Remote Sensing, 26, pp. 2113-2133.
81. Van Coillie, F. M. B., Verbeke, L. P. C. and De Wulf, R. R. (2004) Previously trained neural networks as ensemble members: Knowledge extraction and transfer. International Journal of Remote Sensing, 25, pp. 4843-4850.
82. Varotsos, C. (2005) Power-law correlations in column ozone over Antarctica. International Journal of Remote Sensing, 26, pp. 3333-3342.
83. Varotsos, C. (2005) Modern computational techniques for environmental data; application to the global ozone layer. Lecture Notes in Computer Science, 3516, pp. 504-510.
84. Varotsos, C. and Kirk-Davidoff, D. (2006) Long-memory processes in ozone and temperature variations at the region 60 degrees S-60 degrees N. Atmospheric Chemistry and Physics, 6, pp. 4093-4100.
85. Varotsos, C., Assimakopoulos, M. and Efstathiou, M. (2007) Technical note: Long-term memory effect in the atmospheric CO2 concentration at Mauna Loa. Atmospheric Chemistry and Physics, 7, pp. 629-634.
86. Varotsos, C., Ondov, J. and Efstathiou, M. (2005) Scaling properties of air pollution in Athens, Greece and Baltimore, Maryland. Atmospheric Environment, 39, pp. 4041-4047.
87. Varotsos, C., Ondov, J., Cracknell, A., Efstathiou, M. and Assimakopoulos, M. (2006) Long-range persistence in global aerosol index dynamics. International Journal of Remote Sensing, 27, pp. 3593-3603.
88. Vesanto, J. and Alhoniemi, E. (2000) Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11, pp. 586-602.
89. Vilmann, T., Merenyi, E. and Hammer, B. (2003) Neural maps in remote sensing image analysis. Neural Networks, 16, pp. 389-403.
90. Wagstaff, K., Rogers, S. and Schroedl, S. (2001) Constrained K-means clustering with background knowledge. Proceedings of 8th International Conference Machine Learning, pp. 577-584.
91. Wang, F. (1990) Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28, pp. 194-201.
92. Wang, S. J. and Zhou, C. (2002) Using remote sensing to estimate the change of carbon storage: A case study in the estuary of Yellow River delta. International Journal of Remote Sensing, 23, pp. 1565-1580.
93. Wetmore, L., Heywood, M. I. and Zincir-Heywood, A. N. (2005) Speeding up the self-organizing features map using dynamic subset selection. Neural Processing Letters, 22, pp. 17-32.
94. Wilkinson, G. (2005) Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 433-440.
95. Wilson, H. G., Boots, B. and Millward, A. A. (2002) A comparison of hierarchical and partitional clustering techniques for multispectral image classification. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'02), 3, pp. 1624-1626.
96. Wu, S. and Chow, T. W. S. (2004) Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognition, 37, pp. 175-188.
97. Xiang, M., Hung, C., Pham, M., Kuo, B. and Coleman, T. (2005) Parallelepiped multispectral image classifier using genetic algorithms. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05), 1, p. 4.
98. Xu, R. and Wunsch II, D. (2005) Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16, pp. 645-678.
99. Zhong, Y., Zhang, L. and Li, P. (2005) Multispectral remote sensing image classification based on simulated annealing clonal selection. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'05), 1, pp. 3745-3748.