Active and semisupervised learning for the classification of remote sensing images

IEEE Transactions on Geoscience and Remote Sensing

Volumn 52, Issue 11, 2014, Pages 6937-6956

  Persello, Claudio ^a,b   Bruzzone, Lorenzo ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

Active learning (AL); image classification; remote sensing (RS); sample selection bias; semisupervised learning (SSL); support vector machine (SVM)

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXPERIMENTS; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; REMOTE SENSING; SAMPLING; SUPERVISED LEARNING;

ACTIVE LEARNING; CLASSIFICATION METHODS; CLASSIFICATION OF REMOTE SENSING IMAGE; CONCEPTUAL FRAMEWORKS; SAMPLE SELECTION BIAS; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING (SSL); STATE-OF-THE-ART TECHNIQUES;

SUPPORT VECTOR MACHINES;

ALGORITHM; CONCEPTUAL FRAMEWORK; EXPERIMENTAL STUDY; IMAGE CLASSIFICATION; REMOTE SENSING;

EID: 84902080705     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2305805     Document Type: Article

References (57)

1. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995.
2. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
3. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2001.
4. S. Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
5. G. Camps-Valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis. Hoboken, NJ, USA: Wiley, 2009.
6. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
7. L. Gomez-Chova, G. Camps-Valls, J. Munoz-Mari, and J. Calpe, "Semisupervised image classification with Laplacian support vector machines," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 336-340, Jul. 2008.
8. P. Mitra, B. U. Shankar, and S. K. Pal, "Segmentation of multispectral remote sensing images using active support vector machines," Pattern Recog. Lett., vol. 25, no. 9, pp. 1067-1074, Jul. 2004. (Pubitemid 38763570)
9. S. Rajan, J. Ghosh, and M. Crawford, "An active learning approach to hyperspectral data classification," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 4, pp. 1231-1242, Apr. 2008. (Pubitemid 351459470)
10. B. Demir, C. Persello, and L. Bruzzone, "Batch-mode active-learning methods for the interactive classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 3, pp. 1014-1031, Mar. 2011.
11. S. Patra and L. Bruzzone, "A fast cluster-assumption based active-learning technique for classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1617-1626, May 2011.
12. J. J. Heckman, "Sample selection bias as a specification error," Econometrica, vol. 47, no. 1, pp. 153-161, Jan. 1979.
13. B. Zadrozny, "Learning and evaluating classifiers under sample selection bias," in Proc. 21st Int. Conf. Mach. Learn., 2004, pp. 903-910. (Pubitemid 40290896)
14. R. G. Congalton and K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL, USA: CRC Press, 1999.
15. O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised Learning. Cambridge, MA, USA: MIT Press, 2006.
16. X. Zhu, "Semi-supervised learning literature survey," Univ. Wisconsin-Madison, Madison, WI, USA, 2008, Tech. Rep.
17. H. Scudder, III, "Probability of error of some adaptive pattern-recognition machines," IEEE Trans. Inf. Theory, vol. IT-11, no. 3, pp. 363-371, Jul. 1965.
18. S. Fralick, "Learning to recognize patterns without a teacher," IEEE Trans. Inf. Theory, vol. IT-13, no. 1, pp. 57-64, Jan. 1967.
19. A. Agrawala, "Learning with a probabilistic teacher," IEEE Trans. Information Theory, vol. IT-16, no. 4, pp. 373-379, Jul. 1970.
20. I. Dopido, J. Li, P. R. Marpu, A. Plaza, J. B. Dias, and J. A. Benediktsson, "Semisupervised self-learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 7, pp. 4032-4044, Jul. 2013.
21. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. Workshop Comput. Learn. Theory, 1998, pp. 92-100.
22. V. R. de Sa, "Learning classification with unlabeled data," Adv. Neural Inf. Process. Syst., vol. 6, no. 112-119, 1994.
23. Q. Jackson and D. Landgrebe, "An adaptive method for combined covariance estimation and classification," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1082-1087, May 2002. (Pubitemid 34798917)
24. B. Shahshahani and D. Landgrebe, "The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1087-1095, Sep. 1994.
25. S. Tadjudin and D. Landgrebe, "Robust parameter estimation for mixture model," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 439-445, Jan. 2000. (Pubitemid 30556206)
26. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th ICML, 1999, pp. 200-209.
27. L. Bruzzone, M. Chi, and M. Marconcini, Semisupervised Support Vector Machines for Classification of Hyperspectral Remote Sensing Images, Hyperspectral Data Exploitation: Theory and Applications, C.-I. Chang, Ed. Hoboken, NJ, USA: Wiley, 2007.
28. M. Chi and L. Bruzzone, "Semisupervised classification of hyperspectral images by SVMs optimized in the primal," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1870-1880, Jun. 2007. (Pubitemid 46853295)
29. M. Belkin and P. Niyogi, "Semi-supervised learning on Riemannian manifolds," Mach. Learn., vol. 56, no. 1-3, pp. 209-239, Jun. 2004.
30. D. Tuia, F. Ratle, F. Pacifici, M. Kanevski, and W. Emery, "Active learning methods for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2218-2232, Jul. 2009.
31. M. Li and I. Sethi, "Confidence-based active learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 8, pp. 1251-1261, Aug. 2006. (Pubitemid 46405023)
32. D. D. Lewis and W. A. Gale, "A sequential algorithm for training text classifiers," in Proc. 17th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 1994, pp. 3-12.
33. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines," in Int. Conf. Mach. Learn., 2000, pp. 839-846.
34. C. Campbell, N. Cristianini, and A. Smola, "Query learning with large margin classifiers," in Proc. 17th Int. Conf. Mach. Learn., 2000, pp. 111-118.
35. S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," J. Mach. Learn. Res., vol. 2, pp. 45-66, Mar. 2002.
36. H. S. Seung, M. Opper, and H. Sompolinsky, "Query by committee," in Proc. 5th Annu. Workshop COLT, 1992, pp. 287-294. (Pubitemid 23615454)
37. I. Dagan and S. P. Engelson, "Committee-based sampling for training probabilistic classifiers," in Proc. Int. Conf. Mach. Learn., 1995, pp. 150-157.
38. N. Abe and H. Mamitsuka, "Query learning strategies using boosting and bagging," in Proc. 15th ICML, 1998, pp. 1-9.
39. T. Luo, K. Kramer, D. B. Goldgof, S. Samson, A. Remsen, T. Hopkins, and D. Cohn, "Active learning to recognize multiple types of plankton," J. Mach. Learn. Res., vol. 6, pp. 589-613, 2005.
40. B. Settles, "Active Learning Literature Survey," Univ. Wisconsin-Madison, Madison, WI, USA, Computer Sciences Tech. Rep.1648, 2009.
41. K. Brinker, "Incorporating diversity in active learning with support vector machines," in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 59-66.
42. Z. Xu, K. Yu, V. Tresp, X. Xu, and J. Wang, "Representative sampling for text classification using support vector machines," in Proc. 25th ECIR, 2003, pp. 393-407.
43. H. T. Nguyen and A. Smeulders, "Active learning using pre-clustering," in Proc. 21st ICML, 2004, p. 79.
44. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu, "Batch mode active learning and its application to medical image classification," in Int. Conf. Mach. Learn., 2006, pp. 417-424.
45. Y. Guo and D. Schuurmans, "Discriminative batch mode active learning," in Proc. Neural Inf. Process. Syst., 2007, pp. 593-600.
46. J. Azimi, A. Fern, X. Z. Fern, G. Borradaile, and B. Heeringa, "Batch active learning via coordinated matching," in Proc. Int. Conf. Mach. Learn., 2012, pp. 1-8.
47. W. Di and M. M. Crawford, "View generation for multiview maximum disagreement based active learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1942-1954, May 2012.
48. W. Di and M. M. Crawford, "Active learning via multi-view and local proximity co-regularization for hyperspectral image classification," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 618-628, Jun. 2011.
49. A. K. McCallum and K. Nigam, "Employing EM and pool-based active learning for text classification," in Proc. Int. Conf. Mach. Learn., 1998, pp. 350-358.
50. U. Muslea, S. Minton, and C. Knoblock, "Active + semi-supervised learning = robust multi-view learning," in Proc. 19th Int. Conf. Mach. Learn., 2002, pp. 435-442.
51. G. Tur, D. Hakkani-Tür, and R. E. Schapire, "Combining active and semi-supervised learning for spoken language understanding," Speech Commun., vol. 45, no. 2, pp. 171-186, Feb. 2005. (Pubitemid 40220192)
52. M. Li, R. Wang, and K. Tang, "Combining semi-supervised and active learning for hyperspectral image classification," in Proc. IEEE Symp. Comput. Intell. Data Mining, 2013, pp. 89-94.
53. J. Lin, "Divergence measures based on the Shannon entropy," IEEE Trans. Inf. Theory, vol. 37, no. 1, pp. 145-151, Jan. 1991.
54. B. Aiazzi, S. Baronti, M. Selva, and L. Alparone, "Enhanced Gram-Schmidt spectral sharpening based on multivariate regression of MS and PAN data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2006, pp. 3806-3809. (Pubitemid 47516397)
55. C. Persello, "Interactive domain adaptation for the classification of remote sensing images using active learning," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 736-740, Jul. 2013.
56. L. Bruzzone and M. Marconcini, "Domain adaptation problems: A DASVM classification technique and a circular validation strategy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 770-787, May 2010.
57. L. Bruzzone and M. Marconcini, "Toward an automatic updating of land-cover maps by a domain adaptation SVM classifier and a circular validation strategy," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1108-1122, Apr. 2009.