Hyperspectral Target Detection Using Learned Dictionary

IEEE Geoscience and Remote Sensing Letters

Volumn 12, Issue 7, 2015, Pages 1531-1535

  Niu, Yubin ^a   Wang, Bin ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Convex optimization; hyperspectral imagery (HSI); learned dictionary (LD); sparse representation; target detection (TD)

Indexed keywords

CONVEX OPTIMIZATION; POLLUTION DETECTION; REMOTE SENSING; SPECTROSCOPY;

CONTAMINATION PROBLEM; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL TARGET DETECTION; LEARNED DICTIONARIES; SPARSE APPROXIMATIONS; SPARSE DICTIONARIES; SPARSE REPRESENTATION;

RADAR TARGET RECOGNITION;

EID: 85027928694     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2015.2412142     Document Type: Article

References (14)

1. B. Du and L. Zhang, "A discriminative metric learning based anomaly detection method," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6844-6857, Nov. 2014.
2. B. Du and L. Zhang, "Random-selection-based anomaly detector for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1578-1589, May 2010.
3. L. Wang and C.-I. Chang, "Multiple-window anomaly detection for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 644-658, Apr. 2008.
4. J. Theiler and B. R. Foy, "Effect of signal contamination in matched-filter detection of the signal on a cluttered background," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 98-102, Jan. 2006.
5. S. Mattoeli, M. Diani, and G. Corsini, "Hyperspectral anomaly detection with kurtosis-driven local covariance matrix corruption mitigation," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 532-536, May 2011.
6. L. Zhang, L. Zhang, D. Tao, and X. Huang, "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.
7. S. Matteoli, M. Diani, and G. Corsini, "Impact of signal contamination on the adaptive detection performance of local hyperspectral anomalies,"IEEE Trans. Geosci. Remote Sens., vol. 52, no. 4, pp. 1948-1968, Apr. 2014.
8. 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.
9. L. Fang, S. Li, X. Kang, and J. A. Benediktsson, "Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation,"IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7738-7749, Dec. 2014.
10. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Sparse representation for target detection in hyperspectral imagery," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 629-640, Jun. 2011.
11. A. S. Charles, B. A. Olshausen, and C. J. Rozell, "Sparse coding for spectral signatures in hyperspectral images," in Proc. Asilomar Conf. Signals, Syst. Comput., Nov. 2010, pp. 191-195.
12. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
13. A. S. Charles, B. A. Olshausen, and C. J. Rozell, "Learning sparse codes for hyperspectral imagery," IEEE J. Sel. Topics Signal Process., vol. 5, no. 5, pp. 963-978, Sep. 2011.
14. Y. Zhang, B. Du, and L. Zhang, "A sparse representation-based binary hypothesis model for target detection in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1346-1354, Mar. 2015.