To be or not to be convex? A study on regularization in hyperspectral image classification

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 2015-November, Issue , 2015, Pages 4947-4950

  Tuia, Devis ^a   Flamary, Remi ^b   Barlaud, Michel ^b  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITÉ CÔTE D'AZUR   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84962601646     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2015.7326942     Document Type: Conference Paper

References (16)

1. G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, "Advances in hyperspectral image classification: Earth monitoring with statistical learning methods," IEEE Signal Proc. Mag., vol. 31, no. 1, pp. 45-54, 2014.
2. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, 2009.
3. 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, 2013.
4. M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014-2039, 2011.
5. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Rem. Sens., vol. 51, no. 1, pp. 217-231, 2013.
6. D. Tuia, M. Volpi, M. Dalla Mura, A. Rakotomamonjy, and R. Flamary, "Automatic feature learning for spatiospectral image classification with sparse SVM," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6062-6074, 2014.
7. T. Zhang, "Analysis of multi-stage convex relaxation for sparse regularization," J. Mach. Learn. Res., vol. 11, pp. 1081-1107, 2010.
8. B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in kernel methods: support vector learning, MIT press, 1998.
9. O. Chapelle, "Training a support vector machine in the primal," Neural Comput., vol. 19, no. 5, pp. 1155-1178, 2007.
10. W. Bel Haj Ali, R. Nock, and M. Barlaud, "Boosting stochastic newton with entropy constraint for large-scale image classification," in Proc. ICPR, Stockholm, Sweden, 2014.
11. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Royal Stat. Soc. B, pp. 267-288, 1996.
12. E. J. Candes, M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted 1 minimization," Journal of Fourier analysis and applications, vol. 14, no. 5-6, pp. 877-905, 2008.
13. P. L. Combettes and J.-C. Pesquet, "Proximal splitting methods in signal processing," in Fixed-point algorithms for inverse problems in science and engineering, pp. 185-212. Springer, 2011.
14. P. Gong, C. Zhang, Z. Lu, J. Z. Huang, and J. Ye, "A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems," in Proc. ICML, Atlanta, GE, 2013.
15. M. M. Crawford, D. Tuia, and L. H. Hyang, "Active learning: Any value for classification of remotely sensed data?," Proceedings of the IEEE, vol. 101, no. 3, pp. 593-608, 2013.
16. F. Pacifici, Q. Du, and S. Prasad, "Report on the 2013 IEEE GRSS data fusion contest: Fusion of hyperspectral and LiDAR data," IEEE Remote Sens. Mag., vol. 1, no. 3, pp. 36-38, 2013.