Kernel sparse representation for hyperspectral target detection

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2012, Pages 7484-7487

  Chen, Yi ^a   Nasrabadi, Nasser M ^b   Tran, Trac D ^a  

^a Johns Hopkins University   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTION ALGORITHM; DETECTION PERFORMANCE; FEATURE SPACE; GREEDY ALGORITHMS; HIGH-DIMENSIONAL FEATURE SPACE; HIGHER-ORDER STRUCTURE; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL TARGET DETECTION; JOINT SPARSITY; KERNEL FUNCTION; KERNEL TRICK; RECONSTRUCTION ACCURACY; SPARSE REPRESENTATION; SPATIAL SMOOTHNESS; TARGET CLASS; TARGET DETECTION ALGORITHM; TRAINING SAMPLE;

FACE RECOGNITION; GEOLOGY; REMOTE SENSING; SIGNAL DETECTION; SPECTROSCOPY; TARGET TRACKING; VECTOR SPACES;

SPACE OPTICS;

EID: 84873173002     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2012.6351901     Document Type: Conference Paper

References (16)

1. D. Manolakis and G. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 29-43, Jan. 2002.
2. J. Wright, A. Y. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
3. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Simultaneous joint sparsity model for target detection in hyperspectral imagery," IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 676-680, July 2011.
4. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Processing, special issue on Sparse approximations in signal and image processing, vol. 86, pp. 572-588, Mar. 2006.
5. H. Kwon and N. M. Nasrabadi, "A comparative analysis of kernel subspace target detectors for hyperspectral imagery," EURASIP Journal on Applied Signal Processing, vol. 2007, no. 1, pp. 193-193, Jan. 2007.
6. D. L. Donoho and M. Elad, "Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization," in Proc. of Nat'l Academy of Sciences, Mar. 2003, pp. 2197- 2202.
7. J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. on Information Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
8. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. on Information Theory, vol. 55, no. 5, pp. 2230-2249, May 2009.
9. S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, "Sparse solutions to linear inverse problems with multiple measurement vectors," IEEE Trans. on Signal Processing, vol. 53, no. 7, pp. 2477-2488, July 2005.
10. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, "Algorithms for simultaneous sparse approximation. Part II: Convex relaxation," Signal Processing, special issue on Sparse approximations in signal and image processing, vol. 86, pp. 589-602, Mar. 2006.
11. E. van den Berg and M. P. Friedlander, "Theoretical and empirical results for recovery from multiple measurements," IEEE Trans. on Information Theory, vol. 56, no. 5, pp. 2516-2527, May 2010.
12. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2001.
13. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. on Geoscience and Remote Sensing, 2012, to appear.
14. G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila- Frances, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 1, pp. 93-97, Jan. 2006.
15. "AVIRIS NW Indiana's Indian Pines 1992 data set," https://engineering.purdue.edu/~biehl/ MultiSpec/hyperspectral.html.
16. J. A. Gualtieri and R. F. Cromp, "Support vector machines for hyperspectral remote sensing classification," in Proc. SPIE 27th AIPR Workshop: Advances in Computer-Assisted Recognition, Washington, DC, Oct. 1998, vol. 3584, pp. 221-232.