Improved band similarity-based hyperspectral imagery band selection for target detection

Journal of Applied Remote Sensing

Volumn 9, Issue 1, 2015, Pages

  Zhang, Jing ^a   Cao, Yan ^a   Zhuo, Li ^a   Wang, Chao ^a   Zhou, Qianlan ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

band selection; forward selection; hyperspectral image; pixels selection; similarity metric; target detection

Indexed keywords

CLUSTERING ALGORITHMS; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; PIXELS; REMOTE SENSING; SPECTROSCOPY; TARGET TRACKING;

ADAPTIVE COHERENCE ESTIMATOR; BAND SELECTION; COMPUTATIONAL BURDEN; FORWARD SELECTION; HIGH DIMENSIONALITY; HYPER-SPECTRAL IMAGERIES; RECEIVER OPERATING CHARACTERISTICS; SIMILARITY METRICS;

RADAR TARGET RECOGNITION;

EID: 84924189740     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.9.095091     Document Type: Article

References (20)

1. L. Zhang et al., "Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction," IEEE Trans. Geosci. Remote Sens. 51(1), 242-256 (2013).
2. C. Chang and S. Wang, "Constrained band selection for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens. 44(6), 1575-1585 (2006).
3. K. Kotwal and S. Chaudhuri, "Visualization of hyperspectral images using bilateral filtering," IEEE Trans. Geosci. Remote Sens. 48(5), 2308-2316 (2010).
4. Q. Du, J. Bioucas-Dias, and A. Plaza, "Hyperspectral band selection using a collaborative sparse model," in Proc. IEEE Int. Geosci. Remote Sens. Symp., pp. 3054-3057, IEEE, Munich (2012).
5. K. Bernard et al., "Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach," IEEE Trans. Image Process. 21(4), 2008-2021 (2012).
6. L. Shijin et al., "An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine," Knowl.-Based Syst. 24, 40-48 (2011).
7. A. Martinez-Uso et al., "Clustering-based hyperspectral band selection using information measures," IEEE Trans. Geosci. Remote Sens. 45(12), 4158-4171 (2007).
8. Q. Du and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis," IEEE Geosci. Remote Sens. Lett. 5(4), 564-568 (2008).
9. M. Diani et al., "A new band selection strategy for target detection in hyperspectral images," in Proc. 12th Int. Conf. Knowledge-Based Intelligent Information and Engineering Systems, Vol. 3, pp. 424-431, Springer Berlin Heidelberg, Zagreb (2008).
10. I. ul Haq, X. Xiaojian, and A. Shahzad "Band clustering and selection and decision fusion for target detection in hyperspectral imagery," in IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 1101-1104, IEEE, Taipei (2009).
11. H. Yang, Q. Du, and G. Chen, "Unsupervised hyperspectral band selection using graphics processing units," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 660-668 (2011).
12. Z. Shi, S. Yang, and Z. Jiang, "Hyperspectral target detection using regularized high-order matched filter," Opt. Eng. 50(5), 057201 (2011).
13. L. Wang, X. Jia, and Y. Zhang, "A novel geometry-based feature-selection technique for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett. 4(1), 171-175 (2007).
14. S. Cai, Q. Du, and R. Moorhead, "Hyperspectral imagery visualization using double layers," IEEE Trans. Geosci. Remote Sens. 45(10), 3028-3036 (2007).
15. T. Lin and S. Bourennane, "Hyperspectral image processing by jointly filtering wavelet component tensor," IEEE Trans. Geosci. Remote Sens., 51(6), 3529-3541 (2013).
16. J. M. P. Nascimento and J. M. Bioucas-Dias, "Hyperspectral subspace identification," IEEE Trans. Geosci. Remote Sens. 46(8), 2435-2445 (2008).
17. B. D. Bue, E. Merenyi, and B. Csatho, "Automated labeling of materials in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens. 48(11), 4059-4070 (2010).
18. Y. Du et al., "New hyperspectral discrimination measure for spectral characterization," Opt. Eng. 43(8), 1777-1786 (2004).
19. H. Yang et al., "An efficient method for supervised hyperspectral band selection," IEEE Geosci. Remote Sens. Lett. 8(1), 138-142 (2011).
20. M. E. Winter, "N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data," Proc. SPIE 3753, 266-275 (1999).