Parallel Spatial-Spectral Hyperspectral Image Classification with Sparse Representation and Markov Random Fields on GPUs

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 8, Issue 6, 2015, Pages 2926-2938

  Wu, Zebin ^a,b   Wang, Qicong ^a   Plaza, Antonio ^b   Li, Jun ^c   Sun, Le ^a   Wei, Zhihui ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF EXTREMADURA   (Spain)

^c SUN YAT SEN UNIVERSITY   (China)

Author keywords

Compute unified device architecture (CUDA); graphics processing units (Gpus); hyperspectral imaging; Markov random fields (Mrfs); parallel implementation; sparse representation; spatial spectral classification

Indexed keywords

COMPUTATIONAL EFFICIENCY; COMPUTER GRAPHICS; COMPUTER GRAPHICS EQUIPMENT; MARKOV PROCESSES; PARALLEL ARCHITECTURES; PROBABILITY DISTRIBUTIONS; PROGRAM PROCESSORS; SPECTROSCOPY; STRUCTURAL FRAMES;

COMPUTE UNIFIED DEVICE ARCHITECTURE(CUDA); GRAPHICS PROCESSING UNITS; HYPERSPECTRAL IMAGING; MARKOV RANDOM FIELD; PARALLEL IMPLEMENTATIONS; SPARSE REPRESENTATION; SPECTRAL CLASSIFICATION;

IMAGE CLASSIFICATION;

ALGORITHM; HARDWARE; IMAGE CLASSIFICATION; MARKOV CHAIN; OPTIMIZATION; REMOTE SENSING; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 85027951816     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2413931     Document Type: Article

References (46)

1. 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.
2. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, Suppl. 1, pp. 110-122, Sep. 2009.
3. A. Plaza, J. M. Bioucas-Dias, A. Simic, and W. J. Blackwell, "Foreword to the special issue on hyperspectral image and signal processing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 347-353, Apr. 2012.
4. 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.
5. Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, "SVM-and MRF-based method for accurate classification of hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736-740, Apr. 2010.
6. B. Zhang, S. Li, X. Jia, L. Gao, and M. Peng, "Adaptive Markov random field approach for classification of hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 5, pp. 973-977, May 2011.
7. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814 , Nov. 2008.
8. Y. Gu et al., "Representative multiple kernel learning for classification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 7, pp. 2852-2865, Jul. 2012.
9. X. Sun, Q. Qu, N. M. Nasrabadi, and T. D. Tran, "Structured priors for sparse-representation-based hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 7, pp. 1235-1239, Jul. 2014.
10. J. Liu, Z.Wu, Z.Wei, L. Xiao, and L. Sun, "Spatial-spectral kernel sparse representation for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 6, pp. 2462-2471, Dec. 2013.
11. 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.
12. G. Camps-Valls, N. Shervashidze, and K. M. Borgwardt, "Spatio-spectral remote sensing image classification with graph kernels," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 741-745, Apr. 2010.
13. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 809-823, Mar. 2012.
14. B. Krishnapuram, L. Carin, M. A. T. Figueiredo, and A. J. Hartemink, "Sparse multinomial logistic regression: Fast algorithms and generalization bounds," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 6, pp. 957-968, Jun. 2005.
15. 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.
16. 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.
17. J. Li, P. Reddy Marpu, A. Plaza, J. 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.
18. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recog., vol. 43, no. 7, pp. 2367-2379, Jul. 2010.
19. L. Xu and J. Li, "Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field," IEEE Geosi. Remote Sens. Lett., vol. 11, no. 4, pp. 823-827, Apr. 2014.
20. R. Ji et al., "Spectral-spatial constraint hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1811-1824, Mar. 2014.
21. L. Sun, Z.Wu, J. Liu, and Z.Wei, "A novel supervised method for hyperspectral image classification with spectral-spatial constraints," Chin. J. Electron., vol. 22, no. 3, pp. 1-7, Jul. 2013.
22. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 217-231, Jan. 2013.
23. X. Kang, S. Li, and J. Benediktsson, "Spectral-spatial hyperspectral image classification with edge-preserving filtering," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2666-2677, May 2014.
24. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
25. C. A. Lee et al., "Recent developments in high performance computing for remote sensing: A review," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 508-527, Sep. 2011.
26. A. Plaza, Q. Du, Y. Chang, and R. L. King, "High performance computing for hyperspectral remote sensing," IEEE J. Sel. Topics Signal Process., vol. 4, no. 3, pp. 528-544, Sep. 2011.
27. E. Christophe, J. Michel, and J. Inglada, "Remote sensing processing: From multicore to GPU," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 4, no. 3, pp. 643-652, Sep. 2011.
28. C. Gonzalez, D. Mozos, J. Resano, and A. Plaza, "FPGA implementation of the N-FINDR algorithm for remotely sensed hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 2, pp. 374-388, Feb. 2012.
29. S. Bernabe et al., "Hyperspectral unmixing on GPUs and multi-core processors: A comparison," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1386-1398, Mar. 2013.
30. S. Bernabe, S. Lopez, A. Plaza, and R. Sarmiento, "GPU implementation of an automatic target detection and classification algorithm for hyperspectral image analysis," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 2, pp. 221-225, Mar. 2013.
31. X. Wu, B. Huang, A. Plaza, Y. Li, and C. Wu, "Real-time implementation of the pixel purity index algorithm for endmember identification on GPUs," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 5, pp. 955-959, May 2013.
32. J. M. P. Nascimento, J.M. Bioucas-Dias, J.M. Rodriguez Alves, V. Silva, and A. Plaza, "Parallel hyperspectral unmixing on GPUs," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 3, pp. 666-670, Mar. 2014.
33. A. Barberis, G. Danese, F. Leporati, A. Plaza, and E. Torti, "Real-time implementation of the vertex component analysis algorithm on GPUs," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 2, pp. 251-255, Feb. 2013.
34. J. M. Bioucas-Dias and M. A. T. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing," in Proc. 2nd Workshop WHISPERS, 2010, pp. 1-4.
35. W. Deng, W. Yin, and Y. Zhang, "Group sparse optimization by alternating direction method," Dept. of Comput. Appl. Math., Rice Univ., Tech Rep. TR11-06, 2011.
36. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721-741, Nov. 1984.
37. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
38. M. Schmidt and K. Alahari, "Generalized fast approximate energy minimization via graph cuts: Alpha-Expansion Beta-Shrink moves," in Proc. 27th Conf. Uncertainty Artif. Intell., Barcelona, Spain, Jul. 2011.
39. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/max-flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124-1137, Sep. 2004.
40. Intel Developer Zone. (2014, Jun.) Intel Math Kernel Library Reference Manual [Online]. Available: https://software.intel.com/sites/products/documentation/hpc/mkl/mklman/
41. EM Photonics. (2014, Jun.). CULA Programmer's Guide [Online]. Available: http://www.culatools.com/cula-dense-programmers-guide/
42. NVIDIA Developer Zone. (2014, Jun.). cuBLAS User Guide [Online]. Available: http://docs.nvidia.com/cuda/cublas/index.html
43. M. Anderson, D. Sheffield, and K. Keutzer, "A predictive model for solving small linear algebra problems in GPU registers," in Proc. IEEE 26th Int. Parallel Distrib. Process. Symp. (IPDPS), 2012, pp. 2-13.
44. J. M. Molero, E. M. Garzón, I. Garciá, E. S. Quintana-Ortí, and A. Plaza, "Efficient implementation of hyperspectral anomaly detection techniques on GPUs and multicore processors," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2256-2266, Jun. 2014.
45. L. Sun, Z. Wu, J. Liu, L. Xiao, and Z. Wei, "Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1490-1503, Mar. 2015.
46. G. M. Foody, "Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority," Remote Sens. Environ., vol. 113, no. 8, pp. 1658-1663, Aug. 2009.