Band Weighting via Maximizing Interclass Distance for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters

Volumn 13, Issue 7, 2016, Pages 922-925

  Yan, Cheng ^a   Bai, Xiao ^a   Ren, Peng ^b   Bai, Lu ^c   Tang, Wenzhong ^a   Zhou, Jun ^d  

^a BEIHANG UNIVERSITY   (China)

^b CHINA UNIVERSITY OF PETROLEUM   (China)

^c CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^d GRIFFITH UNIVERSITY   (Australia)

Author keywords

Band weighting; hyperspectral image classification; support vector machines (SVMs)

Indexed keywords

BINS; SAMPLING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

BINARY SUPPORT VECTOR MACHINES; CLASSIFICATION RESULTS; HYPERSPECTRAL IMAGE CLASSIFICATION; INTER-CLASS DISTANCE; OBJECTIVE FUNCTIONS; SPECTRAL DISTANCES; TESTING SAMPLES; WEIGHTING STRATEGIES;

IMAGE CLASSIFICATION;

EID: 84966577672     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2016.2553699     Document Type: Article

References (14)

1. W. Li, S. Prasad, and J. E. Fowler, "Hyperspectral image classification using Gaussian mixture models andMarkov random fields," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 153-157, Jan. 2014.
2. X. Bai, H. Zhang, and J. Zhou, "VHR object detection based on structural feature extraction and query expansion," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6508-6520, Oct. 2014.
3. G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
4. A. Ifarraguerri and C.-I. Chang, "Unsupervised hyperspectral image analysis with projection pursuit," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 6, pp. 2529-2538, Nov. 2000.
5. A. Agarwal, T. El-Ghazawi, H. El-Askary, and J. Le-Moigne, "Efficient hierarchical-PCA dimension reduction for hyperspectral imagery," in Proc. IEEE Int. Symp. Signal Process. Inf. Technol., 2007, pp. 353-356.
6. Q. Du and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 564-568, Oct. 2008.
7. X. Bai, Z. Guo, Y. Wang, Z. Zhang, and J. Zhou, "Semisupervised hyperspectral band selection via spectral-spatial hypergraph model," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2774-2783, Jun. 2015.
8. N. H. Ly, Q. Du, and J. E. Fowler, "Sparse graph-based discriminant analysis for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 3872-3884, Jul. 2014.
9. B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson, "Customizing kernel functions for SVM-based hyperspectral image classification," IEEE Trans. Image Process., vol. 17, no. 4, pp. 622-629, Apr. 2008.
10. A. Chakrabarty, O. Choudhury, P. Sarkar, A. Paul, and D. Sarkar, "Hyperspectral image classification incorporating bacterial foragingoptimized spectral weighting," Artif. Intell. Res., vol. 1, no. 1, p. p63, Aug. 2012.
11. B. Qi, C. Zhao, and G. Yin, "Feature weighting algorithms for classification of hyperspectral images using a support vector machine," Appl. Opt., vol. 53, no. 13, pp. 2839-2846, May 2014.
12. M. Imani and H. Ghassemian, "Feature extraction using weighted training samples," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 7, pp. 1387-1386, Jul. 2015.
13. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-39, Apr. 2011.
14. J. Snyman, Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms, vol. 97. Boston, MA, USA: Springer Sci. Bus. Media, 2005.