Stationary Wavelet Transform based Radiometric Error Correction Technique for NOAA-AVHRR sensor data

  • Abstract
  • Keywords
  • References
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  • Abstract

    Due to the inaccuracy of the sensing devices remote sensing images contain radiometric errors, which can be severe in many cases. Therefore, the preprocessing is an inevitable step in the remote sensing image analysis. This paper presents radiometric errors and evaluates methodologies to retrieve information contained in images by means of filtering in the spatial domain and wavelet domain. Among those, the wavelet techniques are more effective to reduce noise because of their ability to capture the energy of a signal in fewer wavelet coefficients. In this study, Stationary Wavelet Transform (SWT) method and its application to NOAA -18, 19AVHRR/3 channel 3 and channel 4 images to correct radiometric error is presented. Qualitative and quantitative analysis was carried to evaluate the performance of SWT method, both by measuring the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), mean value, standard deviation and by visual inspection. The SWT based method can remove radiometric error effectively and preserves radiometric information to a desirable amount. From the results, SWT based method is better in smoothness and accuracy than the conventional mean filter, median filter and Discrete Wavelet Transform (DWT) based method



  • Keywords

    NOAA-AVHRR images, AVHRR sensor, radiometric errors, Stationary Wavelet Transform (SWT), wavelet thresholding.

  • References

      [1] K. Hiroshi, S. Futoki and K. Shoichi, “The AVHRR Data Processing System in the Center for Atmospheric and Oceanic Studies in the Tohoku University”, Tohoku Geophysics. Journal, Vol. 34, No. 3, pp. 103-114, 1993.

      [2] D. Ehrlich , J. E. Estes & A. Singh, Applications of NOAA-AVHRR 1 km data for environmental monitoring, International Journal of Remote Sensing, Vol. 15, Issue.1, pp. 145-161, May 2007.

      [3] K. Kawano and J.I Kudoh, “Noise Line Detection Method Using Spatial Correlation of NOAA AVHRR Channels”, IEEE, PP. 2377 – 2378, 2006.

      [4] A.N. VAN and A. Yoshimitsu, “Error Correction for NOAA AVHRR Data Using Reference Data” Asian Association on Remote Sensing - 27th Asian Conference on remote Sensing, ACRS, pp. 314-319, 2006.

      [5] A.N.Van and A.Yoshimitsu, “Precise error correction method for NOAA AVHRR image using the same orbital images”, ECTI Transactions on Electrical Eng., Electronics, and Communications, Vol. 5, No. 2, pp.127-136, August 2007.

      [6] W. David, “AVHRR channel-3 noise and methods for its removal”, International. Journal of remote Sensing, Vol. 10, Nos. 4 and 5, pp. 645-65, 1989.

      [7] J. Simpson and S. R. Yhann, “Reduction of Noise in AVHRR Channel 3 Data with Minimum Distortion”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 2, March 1994.

      [8] L. Di and Donald C. Rundquist, “A One Step Algorithm for Correction and Calibration of AVHRR Level l b Data”, Photogrammetric Engineering & Remote Sensing, Vol. 60, No. 2, pp. 165-171, February 1994.

      [9] Y. Dong and S. Xu, “A new directional weighted median filter for removal of random-valued impulse noise”, IEEE Signal Processing Letters, Vol. 14, pp. 193-196, March 2007.

      [10] T. Loupas, W. N. McDicken, and P. L. Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasonic images”, IEEE Transactions on Circuits and Systems, Vol.36, Jan 1989.

      [11] Y.Liu, B. Dang, Y. Li, H. Lin, and H. Ma, “Applications of Savitzky-Golay Filter for Seismic Random Noise Reduction”, Acta Geophysica, Vol. 64, No. 1, pp.101-112, Feb. 2016.

      [12] A. Shukla, Dr R.K. Singh, “Performance analysis of frequency domain filters for noise reduction”, e-Journal of Science & Technology, Vol. 9, No. 5, pp. 167-178, 2014.

      [13] S. G. Chang, B. Yu and M. Vattereli, “Wavelet Thresholding for Multiple Noisy Image Copies,” IEEE Transactions on Image Processing, Vol. 9, No. 9,pp.1631- 1635, 2000.

      [14] K. Kanagaraj, A.P. Subramonian, A. Kandasamy, “Optimal Decomposition Level of Discrete, Stationary and Dual Tree Complex Wavelet Transform for Pixel based Fusion of Multi-focused Images”, Serbian Journal of Electrical Engineering, Vol. 7, No. 1, pp. 81-93, May 2010.

      [15] G. P. Nason, and B. W. Silverman,“The stationary wavelet transform and some statistical applications, ” Lecture Notes in Statistics, 103, pp. 281–299, 1995.

      [16] R. R. Coifman and D. L. Donoho, “Translation invariant de-noising” Lecture Notes in Statistics, 103, pp. 125–150,1995.

      [17] H. Naimi, A. B. Houda A. Mitiche and L. Mitiche, “Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter”, Journal of King Saud University – Computer and Information Sciences, (2015), Vol. 27, pp. 40–45.

      [18] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” Image Processing, IEEE Transactions on, Vol. 13, No. 4, pp. 600–612, 2004.

      Z. Wang and A. Bovik, “A universal image quality index,” Signal Processing Letters, IEEE, Vol. 9, No. 3, pp. 81–84, Mar 2002.




Article ID: 12567
DOI: 10.14419/ijet.v7i2.15.12567

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