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Poster Session 1
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A Simple Sar Speckle Reduction by Wavelet Thresholding
4 The proposed algorithm
The basic idea of speckle reduction by wavelet thresholding is to convert the multiplicative noise to the case of an additive noise. This can be performed by the applications of logarithmic function at the input, and the exponential function at the output, as shown in Figure 1.
Figure 1: block diagram of the proposed algorithm.
5 Experimental results
Experiments were carried out for the proposed algorithm using different SAR images. An example is given in Figure 2. At first, the natural logarithm was taken for each pixel value of the original JERS-1/SAR image, which is shown in figure 2(a). Then the logarithmic image was decomposed by a 2-level wavelet transform. A threshold value was estimated according to eqs. (2)-(3) and used for the soft-thresholding, which was performed on all the high frequency subimages. The exponential function was applied to the reconstructed logarithmic image in order to get back the conventional pixel values of the filtered image, as shown in Figure 29c). An obvious reduction in the speckle can be seen inhomogeneous regions. On the right column, figure 2(b) and (d), are shown the corresponding histograms of the original and the filtered images, respectively.
Figure 2: Experimental results. (a) Original JERS-1/SAR image. (b) Histogram of (a). (c) Filtered image. (d) Histogram of (c).
6 Conclusions
A multriesolution filtering algorithm, based on thresholding the high-frequency wavelet subbands, was proposed for speckle reduction of SAR images. This algorithm is simple, but useful in general SAR image applications
Acknowledgement
The authors wish to thank the National Research Council of Thailand (NRCT) of providing the satellite image data.
Reference
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J.S. Lee, "speckle suppression and Analysis for Synthetic Aperture Radar Images", Optical Engineering, vol. 25, no. 5 pp. 636-645, 1986.
- J.M. Durand, B.J. Gimonet, and J. Perbos, "SAR Data Filtering for Classification", IEEE Trans. Geosci, and Remote Sensing, vol. 25, no. 5 pp. 629-637, 1987.
- C.S. Burrus, R.A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, New Jersey: Prentice-Hall Inc, 1998.
- D.L. Donoho, "De-noising by soft Thresholding", IEEE Trans. Inform. Theory, vol. 41, no. 3, pp. 613-627, 1995.
- S. Mallat, A Wavelet Tour of signal Processing, San Diago: Academic Press 1998.
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