Call for Paper - December 2019 Edition
IJCA solicits original research papers for the December 2019 Edition. Last date of manuscript submission is November 20, 2019. Read More

Maximum Distance Band Selection of Hyperspectral Images

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Shahram Latifi, Steven Wilson
10.5120/ijca2016908214

Shahram Latifi and Steven Wilson. Article: Maximum Distance Band Selection of Hyperspectral Images. International Journal of Computer Applications 133(17):36-43, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Shahram Latifi and Steven Wilson},
	title = {Article: Maximum Distance Band Selection of Hyperspectral Images},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {17},
	pages = {36-43},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Hyperspectral Imaging has been advanced by recent improvements in airborne imaging hardware. Early airborne HSI datasets such as Indian Pines, have a relatively low spatial and spectral resolution and are useful primarily for research purposes. Higher resolution and lower sensor noise has become the industry standard. Since there is more high quality data available, less emphasis can be placed on denoising and pixel unmixing, and the problem becomes one of computational complexity. Therefore, there is a need for preprocessing methods which reduce the amount of raw data processed by target detection algorithms. The purpose of this research is to propose a method of maximum distance automated band selection in order to preprocess hyperspectral image cube data, and present the results when compared to those using the entire data set. The goal is to significantly increase the accuracy of target detection using a Robust Matched Filter (RMF) while at the same time reducing the computational time required to process the data.

References

  1. Purdue Research Foundation, "Aviris image Indian Pine Test Site," 2014, https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html.
  2. Caltech, "AVIRIS Overview," 2000, http://aviris.jpl.nasa.gov/html/overview.html.
  3. SpecTIR Remote Sensing Division, "ProSpecTIR VS," 2011, http://www.spectir.com/wp-content/uploads/2012/02/ProSpecTIR_VS_specs_2011.pdf.
  4. A. Giannandrea, N. Raqueno, D. Messinger, J. Faulring, J. Kerekes, J. van Aardt, K. Canham, S. Hagstrom, E. Ontiveros, A. Gerace, J. Kaufman, K. Vongsy, H. Griffith, and B. Bartlett, "The SHARE 2012 data collection campaign," 2013.
  5. D. Manolakis, R. Lockwood, T. Cooley, J. Jacobson, "Robust Matched Filters for Target Detection in Hyperspectral Imaging Data," Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on. Vol. 1. IEEE, 2006.
  6. O. Rajadell, P. Garcia-Sevilla, V. C. Dinh, and R. P. W. Duin, "Improving Hyperspectral Pixel Classification With Unsupervised Training Data Selection," 2014.
  7. K. Sun, X. Geng, L. Ji, and Y. Lu, "A New Band Selection Method for Hyperspectral Image Based on Data Quality," 2014.
  8. Q. Du, and H. Yang, "Similarity-based unsupervised band selection for hyperspectral image analysis," Geoscience and Remote Sensing Letters, IEEE5.4, 564-568, 2008,
  9. T. Lin, and S. Bourennane. "Hyperspectral image processing by jointly filtering wavelet component tensor," Geoscience and Remote Sensing, IEEE Transactions on 51.6, 3529-3541, 2013,
  10. Y. Qian, F. Yao, and S. Jia, “Band selection for hyperspectral imagery using affinity propagation,” IET Comput. Vis., vol. 3, no. 4, pp. 213–222, 2009,
  11. M. Griffin, and H. H. Burke, "Compensation of hyperspectral data for atmospheric effects," Lincoln Laboratory Journal, 14(1), 29-54, 2003.
  12. Rochester Institute of Technology, Flexviewer, http://barracuda.cis.rit.edu/flexviewer/.
  13. K. N. Liou, An introduction to atmospheric radiation. vol. 84. Academic press, 2002. page 86
  14. H. Yang, and Q. Du, "Fast band selection for hyperspectral imagery," 2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011.
  15. L. Wang, Y. Zhang, and Y. Gu, " Unsupervised Band Selection Method Based on Improved N-FINDR Algorithm for Spectral Unmixing," ISSCAA Preceedings pp.10211024,2006.http://dx.doi.org/10.1109/ISSCAA.2006.1627496
  16. BS ISO 5725-1: "Accuracy (trueness and precision) of measurement methods and results - Part 1: General principles and definitions.", p.1 (1994)

Keywords

hyperspectral, imaging, automated, standoff, target, detection,