Pre-Processing of Hyperspectral Images Using Nonlinear Filters

Open access


In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6


  • 1. AVIRIS: Airborne Visible Infrared Imaging Spectrometer.

  • 2. Manolakis, D., D. Marden, G. Shaw. Hyperspectral Image Processing for Automatic Target Detection Applications. - Lincoln Laboratory Journal, 14, 2003, No. 1,79-116.

  • 3. Ferwerda, J. G. Charting the Quality of Forage: Measuring and Mapping the Variation of Chemical Components in Foliage with Hyperspectral Remote Sensing. - ITC Dissertation, Wageningen University, 126, 2005, 166.

  • 4. Tilling, A. K., et al. Remote Sensing to Detect Nitrogen and Water Stress in Wheat. The Australian Society of Agronomy, 2006.

  • 5. Kamaruzaman, J. Precision Forestry Using Airborne Hyperspectral Imaging Sensor. - Journal of Agricultural Science, 1, 2009, No. 1, 142-147.

  • 6. Ustin, S., D. Roberts, J. Gamon, G. Asner, R. Green. Using Imaging Spectroscopy for Study Ecosystem Processes and Properties. - BioScience, 54, 2004, No. 6, 523-534.

  • 7. Funk, C., J. Theiler, D. Roberts, C. Borel. Clustering to Improve Matched Filter Detection of Weak Gas Plumes in Hyperspectral Thermal Imagery. - IEEE Trans. on Geoscience and Remote Sensing, 39, 2001, No. 7, 1410-1420.

  • 8. Nagao, M., T. Matsuyama. Edge-preserving Smoothing Filters. - Computer Graphics and Image Processing, 9, 1979, No. 4, 394-407.

  • 9. Ramponi, G. The Rational Filter for Image Processing. - IEEE Signal Processing Letters, 3, 1996, No. 3, 63-65.

  • 10. Kroner, S., G. Ramponi, Edge Preserving Noise Smoothing with an Optimized Cubic Filter. Proc. COST-254 Workshop, Ljubljana, 1998, 19-21.

  • 11. Aurich, V., J. Weule. Non-Linear Gaussian Filters Performing Edge Preserving Diffusion. 17th DAGM Symposium, Bielefeld, 1995, 538-545.

  • 12. Yu, Y. Speckle Reducing Anisotropic Diffusion. - IEEE Trans. Imag. Proc., 11, 2002, 1260-1270.

  • 13. Sun, O., A. Hossack, J. Tang, S. Acton. Speckle Reducing Anisotropic Diffusion for 3D Ultrasound Images. - Computerized Medical Imaging and Graphics, 28, 2004, 461-470.

  • 14. Stefan, D. Prostate Ultrasound Image Processing. - Spring, 13, 2007, No. 3, 20-23.

  • 15. Aiazzi, B., L. Alparone, A. Barducci, S. Baronti, P. Marcoionni, I. Pippi, M. Selva. Noise Modelling and Estimation of Hyperspectral Data from Airborne Imaging Spectrometers. - Annals of Geophysics, 49, 2006, No. 1, 1-9.

Information Technologies and Control

The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 36 36 21
PDF Downloads 11 11 9