Active-metric learning for classification of remotely sensed hyperspectral images

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 4, 2016, Pages 1925-1939

  Pasolli, Edoardo ^a   Yang, Hsiuhan Lexie ^b   Crawford, Melba M ^b  

^a UNIVERSITY OF TRENTO   (Italy)

^b PURDUE UNIVERSITY   (United States)

Author keywords

Active learning (AL); Classification; Dimensionality reduction; Hyperspectral images; Large margin nearest neighbor (LMNN); Metric learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXTRACTION; FEATURE EXTRACTION; ITERATIVE METHODS; NEAREST NEIGHBOR SEARCH; REMOTE SENSING; SPECTROSCOPY;

CLASSIFICATION ACCURACY; COMPUTATIONAL TIME; DIMENSIONALITY REDUCTION; HIGH DIMENSIONALITY; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; K-NEAREST NEIGHBORS; LARGE MARGIN NEAREST NEIGHBORS;

ALUMINUM;

ACCURACY ASSESSMENT; DATA ASSIMILATION; DATA SET; IMAGE CLASSIFICATION; NUMERICAL METHOD; SPECTRAL ANALYSIS; SUPERVISED LEARNING;

EID: 84946780584     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2490482     Document Type: Article

References (39)

1. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
2. L. Bruce, C. Koger, and J. Li, "Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2331-2338, Oct. 2002.
3. J. Wang and C. Chang, "Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1586-1600, Jun. 2006.
4. B. Kuo and D. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1096-1105, May 2004.
5. C. M. Bachmann, T. L. Ainsworth, and R. A. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2786-2803, Oct. 2006.
6. M. M. Crawford, L. Ma, and W. Kim, "Exploring nonlinear manifold learning for classification of hyperspectral data," in Optical Remote Sensing. Berlin, Germany: Springer-Verlag, 2011, pp. 207-234.
7. D. Lunga, S. Prasad, M. Crawford, and O. Ersoy, "Manifold-learning-based feature extraction for classification of hyperspectral data: A review of advances in manifold learning," IEEE Signal Process. Mag., vol. 31, no. 1, pp. 55-66, Jan. 2014.
8. S. Yan, "Graph embedding and extensions: A general framework for dimensionality reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40-51, Jan. 2007.
9. Z. Zhang, J. Wang, and H. Zha, "Adaptive manifold learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 253-265, Feb. 2012.
10. L. Ma, M. M. Crawford, X. Yang, and Y. Guo, "Local-manifold-learning-based graph construction for semisupervised hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2832-2844, May 2015.
11. V. N. Balasubramanian, J. Ye, and S. Panchanathan, "Biased manifold embedding: A framework for person-independent head pose estimation," in Proc. CVPR, Jun. 2007, pp. 1-7.
12. B. Settles, Active Learning Literature Survey. Madison, WI, USA: Univ. Wisconsin, 2010.
13. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, "A survey of active learning algorithms for supervised remote sensing image classification," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606-617, Jun. 2011.
14. M. Crawford, D. Tuia, and H. Yang, "Active learning: Any value for classification of remotely sensed data?" Proc. IEEE, vol. 101, no. 3, pp. 593-608, Mar. 2013.
15. D. Tuia and J. Munoz-Mari, "Learning user's confidence for active learning," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 872-880, Feb. 2013.
16. E. Pasolli, F. Melgani, N. Alajlan, and N. Conci, "Optical image classification: A ground-truth design framework," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 6, pp. 3580-3597, Jun. 2013.
17. A. Liu, G. Jun, and J. Ghosh, "Active learning of hyperspectral data with spatially dependent label acquisition costs," in Proc. IGARSS, Jul. 2009, vol. 5, pp. V-256-V-259.
18. E. Pasolli, H. L. Yang, and M. M. Crawford, "Combining active and metric learning for hyperspectral image classification," presented at the 6th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.
19. K. Q. Weinberg and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification," J. Mach. Learn. Res., vol. 10, pp. 207-244, Feb. 2009.
20. Q. Zhang, L. Zhang, Y. Yang, Y. Tian, and L. Weng, "Local patch discriminative metric learning for hyperspectral image feature extraction," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 3, pp. 612-616, Mar. 2014.
21. L. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, "Hyperspectral remote sensing image subpixel target detection based on supervised metric learning," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 4955-4965, Aug. 2014.
22. A. Bellet, A. Habrard, and M. Sebban, "A survey on metric learning for feature vectors and structured data," Tech. Rep., Lab. Hubert Curien, Univ. de Saint-Etienne, St-Etienne, France, 2014. [Online]. Available: http:// arxiv.org/abs/1306.6709
23. D. Tuia, M. Volpi, M. Trolliet, and G. Camps-Valls, "Semisupervised manifold alignment of multimodal remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7708-7720, Dec. 2014.
24. V. N. Vapnik, Statistical Learning Theory. New York, NY, USA: Wiley, 1998.
25. C. Shen and J. Kim, "Positive semidefinite metric learning using boostinglike algorithms," J. Mach. Learn. Res., vol. 13, no. 1, pp. 1007-1036, Apr. 2012.
26. L. Torresani and K. C. Lee, "Large margin component analysis," Adv. Neural. Inf. Process. Syst., vol. 19, pp. 1385-1392, 2007.
27. T. Liu, A. W. Moore, A. Gray, and K. Yang, "An investigation of practical approximate nearest neighbor algorithms," Adv. Neural. Inf. Process. Syst., vol. 17, pp. 825-832, 2005.
28. S. Omohundro, "Efficient algorithms with neural network behavior," Complex Syst., vol. 1, pp. 273-347, 1987.
29. H. S. Seung, M. Opper, and H. Sompolinsky, "Query by committee," in Proc. 5th Annu. Workshop Comput. Learn. Theory, 1992, pp. 287-294.
30. 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.
31. 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.
32. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000.
33. [Online]. Available: http://isomap.stanford.edu/
34. T. Luo et al., "Active learning to recognize multiple types of plankton," J. Mach. Learn. Res., vol. 6, no. 4, pp. 589-613, Apr. 2005.
35. E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W. J. Emery, "SVM active learning approach for image classification using spatial information," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 2217-2233, Apr. 2014.
36. D. Tuia, E. Pasolli, and W. J. Emery, "Using active learning to adapt remote sensing image classifiers," Remote Sens. Environ., vol. 115, no. 9, pp. 2232-2242, Sep. 2011.
37. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Advances in spectral-spatial classification of hyperspectral images," Proc. IEEE, vol. 101, no. 3, pp. 652-675, Mar. 2013.
38. S. Lee and M. M. Crawford, "Hierarchical clustering approach for un-supervised image classification of hyperspectral data," in Proc. IGARSS, Jul. 2004, vol. 2, pp. 941-944.
39. H. L. Yang and M. M. Crawford, "Exploiting spectral-spatial proximity for classification of hyperspectral data on manifold," in Proc. IGARSS, Jul. 2012, pp. 4174-4177.