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BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES

Year 2020, , 53 - 62, 30.06.2020
https://doi.org/10.33769/aupse.676083

Abstract

Due to the high spectral resolution, hyperspectral images need large data storage and processing time. Indeed, its high dimensional structure requires high computational complexity, especially for target detection. In order to overcome these problems, band reduction methods have been proposed. In this paper, we compare PCA and SNR-based band reduction methods to improve target detection performance in hyperspectral images. Experimental results show that band reduction methods not only reduce processing time, but also increase accuracy rate.

References

  • Marwaha, R. and Kumar, A., Target Detection Algorithm for Airborne Thermal Hyperspectral Data, ISPRS Technical Commission VIII Symposium, Volume XL-8, 2014.
  • RIT (Rochester Institute of Technology) 2006, Chooke City Hyperspectral Data Set http://dirsapps.cis.rit.edu/blindtest/download. Last access date: 24.07.2019.
  • Rodarmel, C. and Shan, J., Principal Component Analysis for Hyperspectral Image Classification, Surveying and Land Information Science, 62(2), (2002), 115-122.
  • Gonzalez, R. and Woods, R., Digital Image Processing, Reading, Massachusetts, Addison-Wesley Publishing Company. pp.148-56, 1993.
  • Mallapragada, S., Wong, M. and Hung, C., Dimensionality Reduction of Hyperspectral Images for Classification, Ninth International Conference on Information, Tokyo, Japan, 2018.
  • Vaiphasa, C., Consideration of Smoothing Techniques for Hyperspectral Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 60, Elsevier, New York, (2006), 91-99.
  • ITT Visual Information Solutions, (ENVI User’s Guide), 2009.
  • Jin, X., Paswaters, S. and Cline, H., A Comparative Study of Target Detection Algorithms for Hyperspectral Imagery, Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341W , 2009.
  • Gordon, C., A Generalization of the Maximum Noise Fraction Transform, IEEE Trans. Geosci. Remote Sens., 38, (2000), 608–610.
  • Manolakis, D. and Shaw, G., Detection Algorithms for Hyperspectral İmaging Applications, Signal Processing, 2002.
Year 2020, , 53 - 62, 30.06.2020
https://doi.org/10.33769/aupse.676083

Abstract

References

  • Marwaha, R. and Kumar, A., Target Detection Algorithm for Airborne Thermal Hyperspectral Data, ISPRS Technical Commission VIII Symposium, Volume XL-8, 2014.
  • RIT (Rochester Institute of Technology) 2006, Chooke City Hyperspectral Data Set http://dirsapps.cis.rit.edu/blindtest/download. Last access date: 24.07.2019.
  • Rodarmel, C. and Shan, J., Principal Component Analysis for Hyperspectral Image Classification, Surveying and Land Information Science, 62(2), (2002), 115-122.
  • Gonzalez, R. and Woods, R., Digital Image Processing, Reading, Massachusetts, Addison-Wesley Publishing Company. pp.148-56, 1993.
  • Mallapragada, S., Wong, M. and Hung, C., Dimensionality Reduction of Hyperspectral Images for Classification, Ninth International Conference on Information, Tokyo, Japan, 2018.
  • Vaiphasa, C., Consideration of Smoothing Techniques for Hyperspectral Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 60, Elsevier, New York, (2006), 91-99.
  • ITT Visual Information Solutions, (ENVI User’s Guide), 2009.
  • Jin, X., Paswaters, S. and Cline, H., A Comparative Study of Target Detection Algorithms for Hyperspectral Imagery, Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341W , 2009.
  • Gordon, C., A Generalization of the Maximum Noise Fraction Transform, IEEE Trans. Geosci. Remote Sens., 38, (2000), 608–610.
  • Manolakis, D. and Shaw, G., Detection Algorithms for Hyperspectral İmaging Applications, Signal Processing, 2002.
There are 10 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Murat Şimşek 0000-0002-8648-3693

Hakki Alparslan Ilgın 0000-0003-0112-4833

Publication Date June 30, 2020
Submission Date January 16, 2020
Acceptance Date February 10, 2020
Published in Issue Year 2020

Cite

APA Şimşek, M., & Ilgın, H. A. (2020). BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 62(1), 53-62. https://doi.org/10.33769/aupse.676083
AMA Şimşek M, Ilgın HA. BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. June 2020;62(1):53-62. doi:10.33769/aupse.676083
Chicago Şimşek, Murat, and Hakki Alparslan Ilgın. “BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, no. 1 (June 2020): 53-62. https://doi.org/10.33769/aupse.676083.
EndNote Şimşek M, Ilgın HA (June 1, 2020) BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62 1 53–62.
IEEE M. Şimşek and H. A. Ilgın, “BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 62, no. 1, pp. 53–62, 2020, doi: 10.33769/aupse.676083.
ISNAD Şimşek, Murat - Ilgın, Hakki Alparslan. “BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62/1 (June 2020), 53-62. https://doi.org/10.33769/aupse.676083.
JAMA Şimşek M, Ilgın HA. BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62:53–62.
MLA Şimşek, Murat and Hakki Alparslan Ilgın. “BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 62, no. 1, 2020, pp. 53-62, doi:10.33769/aupse.676083.
Vancouver Şimşek M, Ilgın HA. BAND REDUCTION FOR TARGET DETECTION IN HYPERSPECTRAL IMAGES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62(1):53-62.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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