Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

Machine Learning

Volumn 103, Issue 3, 2016, Pages 343-375

  Appice, Annalisa ^a,b   Guccione, Pietro ^c   Malerba, Donato ^a,b  

^a UNIVERSITY OF BARI   (Italy)

^b Smart Cities and Communities National Lab CINI Consorzio Interuniversitario Nazionale per l Informatica   (Italy)

^c POLITECNICO DI BARI   (Italy)

Author keywords

Collective inference; Ensemble learning; Iterative learning; Relational classification; Transduction

Indexed keywords

IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; ITERATIVE METHODS; PIXELS; SPECTROSCOPY;

APPLICATION SPECIFIC; CLASSIFICATION METHODOLOGIES; COLLECTIVE INFERENCE; ENSEMBLE LEARNING; ITERATIVE LEARNING; SPATIAL NEIGHBORHOODS; TRANSDUCTION; TRANSDUCTIVE INFERENCE;

CLASSIFICATION (OF INFORMATION);

EID: 84961838352     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-016-5559-7     Document Type: Article

References (92)

1. Ablin, R., & Sulochana, C. (2013). A survey of hyperspectral image classification in remote sensing. International Journal of Advanced Research in Computer and Communication Engineering, 2(8), 2986–3000.
2. Antanas, L., van Otterlo, M., Mogrovejo, J. O., Tuytelaars, T., & Raedt, L. D. (2014). There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding. Neurocomputing, 123, 75–85.
3. Appice, A., & Malerba, D. (2014). Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering. Data Mining and Knowledge Discovery, 28(5–6), 1266–1313.
4. Appice, A., Guccione, P., Malerba, D., & Ciampi, A. (2014). Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Information Sciences, 285, 162–180.
5. AVIRIS. 2007. http://aviris.jpl.nasa.gov/
6. Benediktsson, J., Pesaresi, M., & Amason, K. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1940–1949.
7. Bilgic, M., Namata, G. M., & Getoor, L. (2007). Combining collective classification and link prediction. In Proceedings of the Seventh IEEE International Conference on Data Mining Workshops, ICDMW 2007 (pp. 381–386). IEEE Computer Society.
8. Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001 (pp. 19–26). Morgan Kaufmann Publishers Inc.
9. Bovolo, F., Bruzzone, L., & Marconcini, M. (2006). A novel context-sensitive SVM for classification of remote sensing images. In IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006 (pp. 2498–2501).
10. Bruzzone, L., Chi, M., & Marconcini, M. (2006). A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3363–3373.
11. Camps-Valls, G., Bandos Marsheva, T., & Zhou, D. (2007). Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3044–3054.
12. Ceamanos, X., Waske, B., Benediktsson, J., Chanussot, J., & Sveinsson, J. (2009). Ensemble strategies for classifying hyperspectral remote sensing data. In J. Benediktsson, J. Kittler, & F. Roli (Eds.), Multiple Classifier Systems, Lecture Notes in Computer Science (Vol. 5519, pp. 62–71). Berlin: Springer.
13. Ceci, M., & Appice, A. (2006). Spatial associative classification: Propositional vs structural approach. Journal of Intelligent Information Systems, 27(3), 191–213.
14. Ceci, M., Berardi, M., & Malerba, D. (2007). Relational data mining and ILP for document image understanding. Applied Artificial Intelligence, 21(4&5), 317–342.
15. Ceci, M., Appice, A., Viktor, H. L., Malerba, D., Paquet, E., & Guo, H. (2012). Transductive relational classification in the co-training paradigm. In P. Perner (Ed.), Proceedings of the 8th International Conference Machine Learning and Data Mining in Pattern Recognition, MLDM 2012, Lecture Notes in Computer Science (Vol. 7376, pp. 11–25). Springer.
16. Chan, J. C. W., & Paelinckx, D. (2008). Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112(6), 2999–3011.
17. Chang, C. I. (2007). Hyperspectral data exploitation: Theory and applications. New York: Wiley.
18. Chechetka, A., Dash, D., & Philipose, M. (2010). Relational learning for collective classification of entities in images. In Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, AAAI, AAAI Workshops (Vol. WS-10-06).
19. Chen, C., Li, W., Su, H., & Liu, K. (2014). Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sensing, 6(6), 5795–5814.
20. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
21. Dundar, M., Krishnapuram, B., Bi, J., & Rao, R. B. (2007). Learning classifiers when the training data is not IID. In M. M. Veloso (Ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 (pp. 756–761).
22. Fang, M., Yin, J., & Zhu, X. (2013). Transfer learning across networks for collective classification. In Proceedings of the 13th International Conference on on Data Mining, ICDM 2013 (pp. 161–170). IEEE Computer Society.
23. Fauvel, M., Chanussot, J., & Benediktsson, J. (2012). A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognition, 45(1), 381–392.
24. Fauvel, M., Tarabalka, Y., Benediktsson, J., Chanussot, J., & Tilton, J. (2013). Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101(3), 652–675.
25. Geman, S., & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI–6(6), 721–741.
26. Getoor, L. (2005). Link-based classification. In Advanced Methods for Knowledge Discovery from Complex Data, Advanced Information and Knowledge Processing (pp. 189–207). London: Springer.
27. Getoor, L., & Taskar, B. (2007). Introduction to statistical relational learning (adaptive computation and machine learning). Cambridge, MA, London: The MIT Press.
28. Goetz, A., Vane, G., Solomon, J., & Rock, B. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–1153.
29. Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M., Chippendale, B. J., et al. (1998). Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment, 65(3), 227–248.
30. Guccione, P., Mascolo, L., & Appice, A. (2015). Iterative hyperspectral image classification using spectral-spatial relational features. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3615–3627.
31. Huang, C., Davis, L., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725–749.
32. Huang, R., & He, W. (2012). Using tri-training to exploit spectral and spatial information for hyperspectral data classification. In 2012 International Conference on Computer Vision in Remote Sensing, CVRS 20012 (pp. 30–33).
33. Hughes, G. (1968). On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1), 55–63.
34. Isaaks, E. H., & Srivastava, M. R. (1990). An introduction to applied geostatistics. USA: Oxford University Press.
35. Jensen, D., Neville, J., & Gallagher, B. (2004) Why collective inference improves relational classification. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 2004 (pp. 593–598). ACM.
36. Joachims, T. (1999). Transductive inference for text classification using Support Vector Machines. In I. Bratko & S. Dzeroski (Eds.), Proceedings of the 16th International Conference on Machine Learning, (ICML 1999) (pp. 200–209). Morgan Kaufmann.
37. Joachims, T. (2003). Transductive learning via spectral graph partitioning. In T. Fawcett & N. Mishra (Eds.), Proceedings of the 20th International Conference on Machine Learning, ICML 2003 (pp. 290–297). AAAI Press.
38. Khodadadzadeh, M., Li, J., Plaza, A., Gamba, P., Atli Benediktsson, J., & Bioucas-Dias, J. (2014a). A new framework for hyperspectral image classification using multiple spectral and spatial features. In 2014 IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4628–4631).
39. Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucas-Dias, J. M., & Li, X. (2014b). Spectral-spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Transactions of Geoscience and Remote Sensing, 52(10), 6298–6314.
40. Kong, X., Ng, M., & Zhou, Z. H. (2013). Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering, 25(3), 704–719.
41. Krogel, M. A., Rawles, S., Zelezny, F., Flach, P. A., Lavrac, N., & Wrobel, S. (2003). Comparative evaluation of approaches to propositionalization. In T. Horvarth & A. Yamamoto (Eds.), Inductive Logic Programming, Lecture Notes in Computer Science (Vol. 2835, pp. 197–214). Berlin: Springer.
42. Legendre, P. (1993). Spatial autocorrelation: Trouble or new paradigm? Ecology, 74(6), 1659–1673.
43. LeSage, J. H., & Pace, K. (2001). Spatial dependence in data mining. In R. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, & R. Namburu (Eds.), Data mining for scientific and engineering applications (pp. 439–460). Dordrecht: Kluwer Academic.
44. Li, J., Bioucas-Dias, J., & Plaza, A. (2011). Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3947–3960.
45. Li, J., Bioucas-Dias, J., & Plaza, A. (2012). Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 50(3), 809–823.
46. Li, J., Bioucas-Dias, J., & Plaza, A. (2013a). Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Transactions on Geoscience and Remote Sensing, 51(2), 844–856.
47. Li, J., Reddy Marpu, P., Plaza, A., Bioucas-Dias, J., & Atli Benediktsson, J. (2013b). Generalized composite kernel framework for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 51(9), 4816–4829.
48. Li, Y., Guan, C., Li, H., & Chin, Z. (2008). A self-training semi-supervised SVM algorithm and its application in an eeg-based brain computer interface speller system. Pattern Recognition Letters, 29(9), 1285–1294.
49. Malerba, D. (2008). A relational perspective on spatial data mining. International Journal of Data Mining, Modelling and Management, 1(1), 103–118.
50. Malerba, D., Ceci, M., & Appice, A. (2009). A relational approach to probabilistic classification in a transductive setting. Engineering Applications of Artificial Intelligence, 22(1), 109–116.
51. McDowell, L., & Aha, D. W. (2012). Semi-supervised collective classification via hybrid label regularization. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012. Omnipress.
52. McDowell, L., Gupta, K. M., & Aha, D. W. (2007). Case-based collective classification. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference (pp. 399–404). AAAI Press.
53. McDowell, L., Gupta, K. M., & Aha, D. W. (2009). Cautious collective classification. Journal of Machine Learning Research, 10, 2777–2836.
54. Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778–1790.
55. Miao, L., Shuying, Z., Zhang, B., Shanshan, L., & Changshan, W. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47, 389–411.
56. Mizoguchi, F., Ohwada, H., Daidoji, M., & Shirato, S. (1997). Using inductive logic programming to learn rules that identify glaucomatous eyes. In N. Lavrac, E. Keravnou, & B. Zupan (Eds.), Intelligent data analysis in medicine and pharmacology, the Springer international series in engineering and computer science (Vol. 414, pp. 227–242). US: Springer.
57. Munoz-Mari, J., Bovolo, F., Gomez-Chova, L., Bruzzone, L., & Camp-Valls, G. (2010). Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 48(8), 3188–3197.
58. Neville, J., & Jensen, D. (2000). Iterative classification in relational data. In Proceedings of 17th International Joint Conference on Artificial Intelligence. AAAI Press.
59. Neville, J., & Jensen, D. (2007). Relational dependency networks. Journal of Machine Learning Research, 8, 653–692.
60. Neville, J., Simsek, O., & Jensen, D. (2004). Autocorrelation and relational learning: Challenges and opportunities. In Proceedings of Workshop on Statistical Relational Learning (pp. 290–299). AAAI Press.
61. Orkin, M., & Drogin, R. (1990). Vital statistics. New York: McGraw Hill.
62. Pesaresi, M., & Benediktsson, J. (2001). A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 309–320.
63. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, B. Scholkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 61–74). MIT Press.
64. Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(Supplement 1), S110–S122.
65. Ratle, F., Camps-Valls, G., & Weston, J. (2010). Semisupervised neural networks for efficient hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2271–2282.
66. Richards, J. A. (1993). Remote sensing digital image analysis: An introduction (2nd ed.). Secaucus, NJ: Springer.
67. ROSIS & HySpex. (1995). http://messtec.dlr.de/en/technology/dlr-remote-sensing-technology-institute/hyperspectral-systems-airborne-rosis-hyspex/index.php
68. Saha, T., Rangwala, H., & Domeniconi, C. (2012). Multi-label collective classification using adaptive neighborhoods. In Proceedings of the 11th International Conference on Machine Learning and Applications, ICMLA 2012 (Vol. 1, pp. 427–432).
69. Sammut, C., & Zrimec, T. (1998). Learning to classify x-ray images using relational learning. In C. Nedellec & C. Rouveirol (Eds.), Proceedings of the European Conference of Machine Learning, ECML 1998, Lecture Notes in Computer Science (Vol. 1398, pp. 55–60). Berlin: Springer.
70. Seeger, M. (2001). Learning with labeled and unlabeled data. Technical report.
71. Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi-Rad, T. (2008). Collective classification in network data. AI Magazine, 29(3), 93–106.
72. Shahshahani, B., & Landgrebe, D. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 32(5), 1087–1095.
73. Shi, X., Li, Y., & Yu, P. (2011). Collective prediction with latent graphs. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011 (pp. 1127–1136). ACM.
74. Sindhwani, V., & Keerthi, S. S. (2006). Large scale semi-supervised linear SVMs. In E. N. Efthimiadis, S. T. Dumais, D. Hawking, & K. Järvelin (Eds.), Proceedings of the 29th Annual International Conference on Research and Development in Information Retrieval, SIGIR 2006 (pp. 477–484).: ACM.
75. Soille, P. (2003). Morphological image analysis: Principles and applications (2nd ed.). Springer Berlin Heidelberg.
76. Srinivasan, A., & King, R. D. (1999). Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Mining and Knowledge Discovery, 3(1), 37–57.
77. Stojanova, D., Ceci, M., Appice, A., Malerba, D., & Dzeroski, S. (2013). Dealing with spatial autocorrelation when learning predictive clustering trees. Ecological Informatics, 13, 22–39.
78. Sun, S. (2013). A survey of multi-view machine learning. Neural Computing and Applications, 23(7–8), 2031–2038. doi:10.1007/s00521-013-1362-6.
79. Tan, K., Li, E., Du, Q., & Du, P. (2014). Hyperspectral image classification using band selection and morphological profiles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 40–48.
80. Tarabalka, Y., Chanussot, J., & Benediktsson, J. (2010a). Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition, 43(7), 2367–2379.
81. Tarabalka, Y., Fauvel, M., Chanussot, J., & Benediktsson, J. (2010b). SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 7(4), 736–740.
82. Taskar, B., Segal, E., & Koller, D. (2001). Probabilistic classification and clustering in relational data. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, IJCAI 2001 (Vol. 2, pp. 870–876). Morgan Kaufmann Publishers Inc.
83. Taskar, B., Abbeel, P., & Koller, D. (2002). Discriminative probabilistic models for relational data. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, UAI 2002 (pp. 485–492). Morgan Kaufmann Publishers Inc.
84. Vapnik, V. (1998). Statistical Learning Theory. New York: Wiley.
85. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York, NY: Springer.
86. Villa, A., Benediktsson, J., Chanussot, J., & Jutten, C. (2011). Hyperspectral image classification with independent component discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4865–4876.
87. Wang, L., Hao, S., Wang, Q., & Wang, Y. (2014). Semi-supervised classification for hyperspectral imagery based on spatial-spectral label propagation. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 123–137.
88. Waske, B., & Benediktsson, J. (2007). Fusion of support vector machines for classification of multisensor data. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 3858–3866.
89. Weiss, Y. (2001). Comparing the mean field method and belief propagation for approximate inference in MRFs. In M. Opper & D. Saad (Eds.), Advanced Mean Field Methods (pp. 229–243). Cambridge, MA, London: MIT Press.
90. Witten, I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). San Francisco: Morgan Kaufmann.
91. Yanover, C., & Weiss, Y. (2002). Approximate inference and protein folding. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (pp. 84–86). MIT Press.
92. Zelezný, F., & Lavrac, N. (2006). Propositionalization-based relational subgroup discovery with RSD. Machine Learning, 62(1–2), 33–63.