Application of multiple-instance learning for hyperspectral image analysis

IEEE Geoscience and Remote Sensing Letters

Volumn 8, Issue 5, 2011, Pages 889-893

  Bolton, Jeremy ^a   Gader, Paul ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

Hyperspectral image (HSI) analysis; land mine detection; multiple instance (MI) learning (MIL); random set framework; random set framework for MIL (RSF MIL)

Indexed keywords

HYPERSPECTRAL IMAGES; LAND-MINE DETECTION; MULTIPLE-INSTANCE (MI) LEARNING (MIL); RANDOM-SET FRAMEWORK; RANDOM-SET FRAMEWORK FOR MIL (RSF-MIL);

INDEPENDENT COMPONENT ANALYSIS;

UNCERTAINTY ANALYSIS;

EID: 80052338576     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2011.2135330     Document Type: Article

References (17)

1. T. Dietterich, R. Lathrop, and T. Lozano-Párez, "Solving the multiple instance problem with axis-parallel rectangles," Artif. Intell., vol. 89, no. 1/2, pp. 31-71, Jan. 1997.
2. O. Maron and T. Lozano-Párez, "A framework for multiple-instance learning," in Proc. Adv. Neural Inf. Process. Syst., 1998, pp. 570-576.
3. V. Raykar, B. Krishnapuram, J. Bi, M. Dundar, and R. Rao, "Bayesian multiple instance learning: Automatic feature selection and inductive transfer," in Proc. 25th Int. Conf. Mach. Learn., New York, 2008, pp. 808-815.
4. J. Bolton and P. Gader, "Multiple instance learning for hyperspectral image analysis," in Proc. IEEE IGARSS, 2010, pp. 4232-4235.
5. P. Torrione, C. Ratto, and L. Collins, "Multiple instance and context dependent learning in hyperspectral data," in Proc. IEEE Workshop Hyperspectral Image Signal Process.: Evolution Remote Sens., 2009, pp. 1-4.
6. G. Camps-Valls, T. Bandos Marsheva, and D. Zhou, "Semi-supervised graph-based hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3044-3054, Oct. 2007.
7. M. Chi and L. Bruzzone, "Classification of hyperspectral data by continuation semi-supervised SVM," in Proc. IEEE IGARSS, Jul. 2007, pp. 3794-3797.
8. S. Raj, J. Ghosh, and M. Crawford, "An active learning approach to knowledge transfer for hyperspectral data analysis," in Proc. IEEE IGARSS, Jul. 2006, pp. 541-544.
9. D. Tuia, F. Ratle, F. Pacifici, A. Pozdnoukhov, M. Kanevski, F. Del Frate, D. Solimini, and W. Emery, "Active learning of very-high resolution optical imagery with SVM: Entropy vs margin sampling," in Proc. IEEE IGARSS, Jul. 2008, vol. 4, pp. IV-73-IV-76.
10. D. Tuia, F. Ratle, F. Pacifici, M. Kanevski, andW. Emery, "Active learning methods for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2218-2232, Jul. 2009.
11. L. Bruzzone and C. Persello, "Active learning for classification of remote sensing images," in Proc. IEEE IGARSS, Jul. 2009, vol. 3, pp. III-693-III-696.
12. A. Zare and P. Gader, "Pattern recognition using functions of multiple instances," in Proc. 20th ICPR, 2010, pp. 1092-1095.
13. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1988.
14. J. Bolton and P. Gader, "Random set framework for multiple instance learning," Inf. Sci., vol. 181, no. 11, pp. 2061-2070, Jun. 2011.
15. J. Bolton and P. Gader, "Cross entropy optimization of the random set framework for multiple instance learning," in Proc. 20th ICPR, 2010, pp. 3907-3910.
16. P. Lucey, T. Williams, M. Mignard, J. Julian, D. Kobubun, G. Allen, D. Hampton,W. Schaff,M. Schlangen, E.Winter,W. Kendall, A. Stocker, K. Horton, and A. Bowman, "AHI: An airborne long-wave infrared hyperspectral imager," Proc. SPIE, vol. 3431, pp. 36-43, 1998.
17. M. Tipping, "Relevance vector machine," U.S. Patent 6 633 857, Oct. 14, 2003.