Speckle Reduction Of Synthetic Aperture Radar Images Research Articles

Adaptively weighted nonlocal means and TV minimization for speckle reduction in SAR images

Speckle noise reduction is an important issue in synthetic aperture radar (SAR) imaging. Because SAR images are distinct in being complex valued and susceptible to corruption owing to multiplicative fluctuations, specialized methods for speckle reduction are needed. Techniques based on nonlocal means perform denoising by exploiting the natural redundancy of patterns within an image. They calculate a weighted average of pixels whose neighborhoods are close to one another, where this significantly reduces noise while preserving most image content. While this method performs well on flat areas and textures, its results are excessively smooth in low-contrast areas, and leave residual noise around edges and singular structures. Another variational denoising method uses total variation (TV) minimization to restore regular images but is prone to excessively smooth textures, the staircasing effect, and contrast losses. Our proposed model is intended for the logarithmic domain of SAR data, and combines the above two methods by minimizing an adaptive TV using a nonlocal data fidelity term. In the variational functionals developed here, weighted parameters of nonlocal regularization are adaptively tuned based on local heterogeneity information and noise in the images. A fast iterative shrinkage/thresholding algorithm (FISTA) is then used to solve the optimization problem. The results of experiments on real SAR images verify the effectiveness of the proposed method in terms of speckle reduction.

Speckle Reduction in SAR Images by Using Homogeneity NeighShrink

Synthetic Aperture Radar (SAR) image suffers from severe artifacts caused by speckle noise which is multiplicative in nature. Some of the adaptive filters such as the Lee filter, the Frost filter and the Kuan filter are the well known speckle filters. These filters adapt the filter coefficients based on the pixels within a fixed moving window. Though it removes speckle noise well in the homogeneous regions, it leaves noise in the heterogeneous areas to preserve the edges and fine details or smoothes the edges to remove the noise in this area. In order to reduce the speckle noise with the preservation of edges, a new method based on wavelet analysis and adaptive mean filtering is proposed in this paper. A comparative study with other methods show that proposed method is better in preserving edges and fine details while reducing the speckle noise.

A Hybrid Method of SAR Speckle Reduction Based on Geometric-Structural Block and Adaptive Neighborhood

Given the improvement of synthetic aperture radar (SAR) imaging technologies, the resolution of SAR image is largely improved and the variation of backscatter amplitude should be considered in SAR image processing. In this paper, considering the spatial geometric properties of SAR image in gray pixel space and the sample selection in the estimation of true signal, local directional property of each pixel is explored with the help of SAR sketching method, and two specially designed filters are integrated for adaptive speckle reduction of SAR images. Specifically, based on the sketch map of a SAR image, the orientation of the sketch point lying at each sketch segment is assigned to the corresponding pixel, and thus all pixels of the SAR image are classified as the directional pixels and the nondirectional pixels. For the directional pixels, given the significant directionality of its neighborhood, a geometric-structural block (GB) is built to center on it and GB-wised nonlocal means filter is designed to estimate the true values of all pixels contained in the GB. Moreover, using the local orientation, the whole image is adopted as the searching range to search the similar GBs. For the nondirectional pixels, based on the locally estimated equivalent number of looks, a novel pixel-based metric is proposed to determine the local adaptive neighborhood (AN) with which an AN-based filter is developed to estimate its true value. Besides, since some nondirectional pixels are contained in GBs, a Bayesian-based fusion strategy is designed for the fusion of their estimated values. In the experiments, three synthetic speckled images and five real SAR images [obtained with different resolutions (e.g., 3, 1, and 0.1 m) and different bands (e.g., X-band, C-band, and Ka-band)] are used for evaluation and analysis. Owing to the usage of local spatial geometric property and the combination of two different filters, the proposed method shows a reasonable performance among the comparison methods, in terms of the speckle reduction and the details’ preservation.

A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images

<p>In this paper, we present a novel approach for joint decorrelation<br />and despeckling of synthetic aperture radar (SAR) imagery. An iterative<br />maximum a posterior estimation is performed to obtain the<br />correlation and speckle-free SAR data, which incorporates a correlation<br />model which realistically explores the physical correlated<br />process of speckle noise on signal in SAR imaging. The correlation<br />model is determined automatically via Bayesian estimation in the<br />log-Fourier domain and patch-wise computation is used to account<br />for spatial nonstationarities existing in SAR data. The proposed<br />approach is compared to a state-of-the-art despeckling technique<br />using both simulated and real SAR data. Experimental results illustrate<br />its improvement in preserving the structural detail, especially<br />the sharpness of the edges, when suppressing speckle noise.</p>

Application of special-purpose artificial neural networks for speckle reduction in SAR images

Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures.

Wavelet Denoising of SAR Images Corrupted by Speckle Noise Using Cycle Spinning

A novel and efficient speckle noise reduction algorithm based on wavelet transform by cycle spinning for removing speckle of unknown variance and minimizing the effect of pseudo-Gibbs phenomena from Synthetic Aperture Radar (SAR) images is proposed. Therefore, we show that the sub-band decompositions of logarithmically transformed SAR images. Then, we process and reconstruct multi-resolution wavelet coefficients by wavelet-threshold using cycle spinning, a technique estimating the true images as the linear average of individual estimates derived from wavelet thresholded translated versions of the noise images. Experimental results show that the proposed de-noising algorithm is possible to achieve an excellent balance between suppresses speckle effectively and weaken as many image Gibbs phenomena as possible. Quantitative and qualitative comparisons of the results obtained by the new method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction in SAR images.

A Level Set Filter for Speckle Reduction in SAR Images

Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR) image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can be cast as a formulation of anisotropic diffusion function; furthermore, we continued to deduce it into a level set formulation. Level set flow into the method allows the front interface to propagate naturally with topological changes, where the speed is proportional to the curvature of the intensity contours in an image. Hence, small speckle will disappear quickly, while large scale interfaces will be slow to evolve. Secondly, for preserving finer detailed structures in images when smoothing the speckle, the evolution is switched between minimum or maximum curvature speed depending on the scale of speckle. The proposed method has been illustrated by experiments on simulation image and ERS-2 SAR images under different circumstances. Its advantages over the traditional speckle reduction filter approaches have also been demonstrated.

Undecimated double density wavelet transform based speckle reduction in SAR images

This paper describes an efficient and adaptive method of threshold estimation for removing Speckle noise from Synthetic Aperture Radar (SAR) images, based on Undecimated Double Density Wavelet Transform (UDDWT). Here the performance of image denoising algorithm is well improved by fixing different optimum threshold values for each wavelet coefficient. The choice of the estimation of the threshold value is carried out by analyzing the statistical parameters of the wavelet subband coefficients like Arithmetic Mean, Geometric Mean and Standard Deviation. Here the image is first decomposed into many subbands using UDDWT. Then based upon the statistical parameters of the wavelet coefficients of subbands, threshold values are found out for each wavelet coefficients. This threshold value is used in Soft Thresholding Technique to remove the noisy wavelet coefficients. Then the inverse transform is applied to get the denoised image. Evaluation parameters like peak signal to noise ratio, standard deviation to mean ratio and Edge Preservation Factor have been used for evaluating the performance of the proposed technique quantitatively. Experimental results on several benchmark images by using the proposed method show that, the proposed method yields significantly superior image quality. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

An automated region growing algorithm for segmentation of texture regions in SAR images

Synthetic Aperture Radar (SAR) images have a high geomorphologic information content. Due to the particular operation of this sensor the geomorphologic features of the Earth's surface are enhanced, hence providing valuable detail related to texture of terrain. In this work, speckle reduction in SAR images is considered first by applying selected filters to the original image. The effectiveness of the filtering was qualitatively evaluated for the whole image, and quantitatively estimated on selected smooth and rough texture areas. The quantitative evaluation was done by calculation of image parameters such as contrast, edge sharpening and speckle reduction. This analysis indicated that the geometric filter performs the best for SAR images. The filtered image was input into an automated region growing algorithm that produced segmented texture objects. The texture model and the parameters of the algorithm are based upon the co-occurrence matrix. From this, a new texture distance is introduced. This distance is used to quantify the optimal parameters of the automated algorithm and to determine the effectiveness of the segmentation. Once the image is segmented the morphology of texture regions is obtained, this comprises the following parameters: area, perimeter and fractal dimension. A texture distance between regions is also calculated in order to estimate the texture separation of these regions. A detailed example is provided to illustrate the methodology proposed in this research.