Spatial coherence-based batch-mode active learning for remote sensing image classification

IEEE Transactions on Image Processing

Volumn 24, Issue 7, 2015, Pages 2037-2050

  Shi, Qian ^a   Du, Bo ^b   Zhang, Liangpei ^a  

^a Mapping and Remote Sensing   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

active learning; hyperspectral images; image classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; IMAGE RECONSTRUCTION; ITERATIVE METHODS; REMOTE SENSING; SAMPLING; SPECTROSCOPY; TARGET TRACKING;

ACTIVE LEARNING; BATCH MODE ACTIVE LEARNING; HYPER-SPECTRAL IMAGES; MEAN-SHIFT CLUSTERING; MEAN-SHIFT SEGMENTATION; REMOTE SENSING APPLICATIONS; REMOTE SENSING IMAGE CLASSIFICATION; STATE-OF-THE-ART METHODS;

IMAGE CLASSIFICATION;

EID: 84927670473     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2015.2405335     Document Type: Article

References (55)

1. T. Yamazaki and D. Gingras, "Image classification using spectral and spatial information based on MRF models," IEEE Trans. Image Process., vol. 4, no. 9, pp. 1333-1339, Sep. 1995.
2. K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, "Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach," IEEE Trans. Image Process., vol. 21, no. 4, pp. 2008-2021, Apr. 2012.
3. J. E. Fowler and Q. Du, "Anomaly detection and reconstruction from random projections," IEEE Trans. Image Process., vol. 21, no. 1, pp. 184-195, Jan. 2012.
4. J. Li, P. Reddy Marpu, A. Plaza, J. M. Bioucas-Dias, and J. Atli Benediktsson, "Generalized composite kernel framework for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 9, pp. 4816-4829, Sep. 2013.
5. B. Settles, Active Learning Literature Survey. Madison, WI, USA: Univ. Wisconsin-Madison, 2010.
6. J. Tang, Z.-J. Zha, D. Tao, and T.-S. Chua, "Semantic-gap-oriented active learning for multilabel image annotation," IEEE Trans. Image Process., vol. 21, no. 4, pp. 2354-2360, Apr. 2012.
7. J.-G. Wang, E. Sung, and W.-Y. Yau, "Active learning for solving the incomplete data problem in facial age classification by the furthest nearest-neighbor criterion," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2049-2062, Jul. 2011.
8. D. P. Williams, "Bayesian data fusion of multiview synthetic aperture sonar imagery for seabed classification," IEEE Trans. Image Process., vol. 18, no. 6, pp. 1239-1254, Jun. 2009.
9. D. MacKay, "Information-based objective functions for active data selection," Neural Comput., vol. 4, no. 4, pp. 590-604, Jul. 1992.
10. D. D. Lewis and J. Catlett, "Heterogeneous uncertainty sampling for supervised learning," in Proc. 11th Int. Conf. Mach. Learn., 1994, pp. 148-156.
11. D. A. Cohn, Z. Ghahramani, and M. I. Jordan. (1996). "Active learning with statistical models." [Online]. Available: http://arxiv.org/abs/cs/9603104
12. A. Vlachos, "A stopping criterion for active learning," Comput. Speech Lang., vol. 22, no. 3, pp. 295-312, Jul. 2008.
13. 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.
14. K. Brinker, "Incorporating diversity in active learning with support vector machines," in Proc. Int. Conf. Mach. Learn. Workshop, 2003, p. 59.
15. Z. Xu, K. Yu, V. Tresp, X. Xu, and J. Wang, "Representative sampling for text classification using support vector machines," Adv. Inf. Retr., vol. 2633, pp. 393-407, Apr. 2003.
16. H. T. Nguyen and A. Smeulders, "Active learning using pre-clustering," in Proc. 21st Int. Conf. Mach. Learn., 2004, p. 79.
17. H. S. Seung, M. Opper, and H. Sompolinsky, "Query by committee," in Proc. 5th Annu. Workshop Comput. Learn. Theory, 1992, pp. 287-294.
18. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Mach. Learn., vol. 28, nos. 2-3, pp. 133-168, Aug./Sep. 1997.
19. S. Argamon-Engelson and I. Dagan. (2011). "Committee-based sample selection for probabilistic classifiers." [Online]. Available: http://arxiv.org/abs/1106.0220
20. N. Abe and H. Mamitsuka, "Query learning strategies using boosting and bagging," in Proc. 15th Int. Conf. Mach. Learn. (ICML), 1998, pp. 1-9.
21. P. Melville and R. J. Mooney, "Diverse ensembles for active learning," in Proc. Int. Conf. Mach. Learn. Workshop, 2004, p. 584.
22. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, "Text classification from labeled and unlabeled documents using EM," Mach. Learn., vol. 39, nos. 2-3, pp. 103-134, May 2000.
23. T. Luo et al., "Active learning to recognize multiple types of plankton," in Proc. 17th Int. Conf. Pattern Recognit. (ICPR), Aug. 2004, pp. 478-481.
24. N. Roy and A. McCallum, "Toward optimal active learning through Monte Carlo estimation of error reduction," in Proc. ICML, Williamstown, MA, USA, 2001, pp. 441-448.
25. C. Campbell, N. Cristianini, and A. Smola, "Query learning with large margin classifiers," in Proc. Int. Conf. Mach. Learn. Workshop, 2000, pp. 111-118.
26. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines," in Proc. Int. Conf. Mach. Learn. Workshop, 2000, pp. 839-846.
27. 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.
28. M. Ferecatu and N. Boujemaa, "Interactive remote-sensing image retrieval using active relevance feedback," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 4, pp. 818-826, Apr. 2007.
29. M. Volpi, D. Tuia, and M. Kanevski, "Memory-based cluster sampling for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 8, pp. 3096-3106, Aug. 2012.
30. S. Patra and L. Bruzzone, "A fast cluster-assumption based activelearning technique for classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1617-1626, May 2011.
31. Z. Li, J. Liu, Y. Yang, X. Zhou, and H. Lu, "Clustering-guided sparse structural learning for unsupervised feature selection," IEEE Trans. Knowl. Data Eng., vol. 26, no. 9, pp. 2138-2150, Sep. 2014.
32. G. Jun, R. R. Vatsavai, and J. Ghosh, "Spatially adaptive classification and active learning of multispectral data with Gaussian processes," in Proc. IEEE Int. Conf. Data Mining Workshops (ICDMW), 2009, pp. 597-603.
33. A. Liu, G. Jun, and J. Ghosh, "Active learning of hyperspectral data with spatially dependent label acquisition costs," in Proc. IEEE Int. Geosci. Remote Sens. Symp., vol. 5. Jul. 2009, pp. V-256-V-259.
34. B. Demir, L. Minello, and L. Bruzzone, "A cost-sensitive active learning technique for the definition of effective training sets for supervised classifiers," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Munich, Germany, Jul. 2012, pp. 1781-1784.
35. D. Tuia, J. Muñoz-Marí, and G. Camps-Valls, "Remote sensing image segmentation by active queries," Pattern Recognit., vol. 45, no. 6, pp. 2180-2192, Jun. 2012.
36. V. Walter, "Object-based classification of remote sensing data for change detection," ISPRS J. Photogramm. Remote Sens., vol. 58, nos. 3-4, pp. 225-238, Jan. 2004.
37. M. M. Crawford, D. Tuia, and H. L. Yang, "Active learning: Any value for classification of remotely sensed data?" Proc. IEEE, vol. 101, no. 3, pp. 593-608, Mar. 2013.
38. J. Muñoz-Marí, D. Tuia, and G. Camps-Valls, "Semisupervised classification of remote sensing images with active queries," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3751-3763, Oct. 2012.
39. M. Li and I. K. Sethi, "Confidence-based active learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 8, pp. 1251-1261, Aug. 2006.
40. D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, "Active learning methods for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2218-2232, Jul. 2009.
41. A. Vlachos, "A stopping criterion for active learning," Comput. Speech Lang., vol. 22, no. 3, pp. 295-312, Jul. 2008.
42. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 18, pp. 790-799, Aug. 1995.
43. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002.
44. D. Comaniciu, V. Ramesh, and P. Meer, "The variable bandwidth mean shift and data-driven scale selection," in Proc. 8th IEEE Int. Conf. Comput. Vis., Jul. 2001, pp. 438-445.
45. X. Huang and L. Zhang, "An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173-4185, Dec. 2008.
46. C.-I. Chang, Hyperspectral Data Exploitation: Theory and Applications. New York, NY, USA: Wiley, 2007.
47. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 27:1-27:27, Apr. 2011.
48. V. Cherkassky and Y. Ma, "Practical selection of SVM parameters and noise estimation for SVM regression," Neural Netw., vol. 17, no. 1, pp. 113-126, Jan. 2004.
49. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Hyperspectral image segmentation using a new Bayesian approach with active learning," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3947-3960, Oct. 2011.
50. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 844-856, Feb. 2013.
51. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4085-4098, Nov. 2010.
52. J. Tang, R. Hong, S. Yan, T.-S. Chua, G.-J. Qi, and R. Jain, "Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images," ACM Trans. Int. Syst. Technol., vol. 2, no. 2, p. 14, Feb. 2011.
53. Q. Shi, L. Zhang, and B. Du, "Semisupervised discriminative locally enhanced alignment for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 9, pp. 4800-4815, Sep. 2013.
54. B. Du, L. Zhang, L. Zhang, T. Chen, and K. Wu, "A discriminative manifold learning based dimension reduction method for hyperspectral classification," Int. J. Fuzzy Syst., vol. 14, no. 2, pp. 272-277, Jun. 2012.
55. N. Wang, B. Du, L. Zhang, and L. Zhang, "An abundance characteristicbased independent component analysis for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 416-428, Jan. 2015.