Abstract
Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.
Highlights
Over the last two decades, there is still growing interests in Synthetic aperture radar (SAR) imaging for its importance in various applications, such as search-and-rescue, high-resolution surface mapping, automatic target recognition, and mine detection
We present the complete despeckling algorithm based on multiscale products
We compare the performance of our proposed algorithm with other methods including hard thresholding method based on discrete wavelet transform (DWT), multiscale products method based on DWT, and the method used in [16]
Summary
Over the last two decades, there is still growing interests in SAR imaging for its importance in various applications, such as search-and-rescue, high-resolution surface mapping, automatic target recognition, and mine detection. The presence of speckle noise in SAR images reduces the detection ability of targets and makes scene analysis and understanding very difficult. Many spatial-domain adaptive despeckling algorithms [2,3,4,5,6] have been proposed in the past few years. Most of these methods model the multiplicative noise and scene with certain models, design a despeckling filter or estimator based on some criterions, and recover the noise-free images from the observations. In the Lee filter [4], the multiplicative model is first approximated by a linear combination of the local mean and the observed pixel. The minimum mean-square error (MMSE) criterion is applied to determine the weighting constant used to construct the filter
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