The Nature of Speckle Noise
Speckle is a granular 'noise' that inherently exists in and degrades the quality of the active radar, synthetic aperture radar (SAR), and medical ultrasound images.
The vast majority of surfaces, synthetic or natural, are extremely rough on the scale of the wavelength. Images obtained from these surfaces by coherent imaging systems such as laser, SAR, and ultrasound suffer from a common phenomena called speckle. Speckle, in both cases, is primarily due to the interference of the returning wave at the transducer aperture. The origin of this noise is seen if we model our reflectivity function as an array of scatterers. Because of the finite resolution, at any time we are receiving from a distribution of scatterers within the resolution cell. These scattered signals add coherently; that is, they add constructively and destructively depending on the relative phases of each scattered waveform. Speckle noise results from these patterns of constructive and destructive interference shown as bright and dark dots in the image [1]
Speckle noise in SAR is generally serious, causing difficulties for image interpretation.[2][3] It is caused by coherent processing of backscattered signals from multiple distributed targets. In SAR oceanography, for example, speckle noise is caused by signals from elementary scatterers, the gravity-capillary ripples, and manifests as a pedestal image, beneath the image of the sea waves.[4][5]
The speckle can also represent some useful information, particularly when it is linked to the laser speckle and to the dynamic speckle phenomenon, where the changes of the speckle pattern, in time, can be a measurement of the surface's activity.
Speckle Noise Reduction
Several different methods are used to eliminate speckle noise, based upon different mathematical models of the phenomenon.[4] One method, for example, employs multiple-look processing (a.k.a. multi-look processing), averaging out the speckle noise by taking several "looks" at a target in a single radar sweep.[2][3] The average is the incoherent average of the looks.[3]A second method involves using adaptive and non-adaptive filters on the signal processing (where adaptive filters adapt their weightings across the image to the speckle level, and non-adaptive filters apply the same weightings uniformly across the entire image). Such filtering also eliminates actual image information as well, in particular high-frequency information, and the applicability of filtering and the choice of filter type involves tradeoffs. Adaptive speckle filtering is better at preserving edges and detail in high-texture areas (such as forests or urban areas). Non-adaptive filtering is simpler to implement, and requires less computational power, however.[2][3]
Improving Titan SAR Images with Probabilistic Despeckle
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| Radar view of Ligeia Mare, a large hydrocarbon sea on Titan. The original version is on the left and the enhanced, “despeckled” version is on the right. Image Credit: NASA/JPL-Caltech/ASI |
New Technique Provides Better, Clearer Radar Images of Titan’s Amazing Surface « AmericaSpace
Saturn’s largest moon Titan is a fascinating world, uniquely alien yet eerily Earth-like in many ways, with its rain, rivers, lakes, seas, and massive sand dunes. But in this extremely cold environment, it is liquid methane and ethane which act as “water,” mimicking the hydrological cycle on Earth. Also, due to the perpetual and global hazy cloud cover, the only way to see these features from orbit is by using radar, which is what the Cassini spacecraft has done on a regular basis for quite a few years now. As good as they are, though, the radar images contain electronic [speckle] noise, which reduces sharpness and clarity. But now a new technique is letting planetary scientists see Titan’s surface more clearly than ever before.A New Way to View Titan: 'Despeckle' It | NASA
The technique is referred to as “despeckling” and produces cleaner images than the original radar images from the Cassini Synthetic Aperture Radar (SAR) instrument on Cassini. It uses an algorithm to modify the noise in the images, making it easier to see small-scale features or changes in the landscape. The idea was initiated by Antoine Lucas while he was working with members of the Cassini imaging team. He was a postdoctoral researcher at the California Institute of Technology in Pasadena at the time.
New imaging technique reveals Titan in unprecedented detail | Astronomy Now
Despeckling Cassini’s radar images has a variety of scientific benefits. Lucas and colleagues have shown that they can produce 3-D maps, called digital elevation maps, of Titan’s surface with greatly improved quality. With clearer views of river channels, lake shorelines and windswept dunes, researchers are also able to perform more precise analyses of processes shaping Titan’s surface. And Lucas suspects that the speckle noise itself, when analysed separately, may hold information about properties of the surface and subsurface.
A despeckle filter for the Cassini synthetic aperture radar images of Titan's surface BRATSOLIS_PSS_2012.pdf
Cassini synthetic aperture radar (SAR) images of Titan, the largest satellite of Saturn, reveal surface features with shapes ranging from quasi-circular to more complex ones, interpreted as liquid hydrocarbon deposits assembled in the form of lakes or seas. One of the major problems hampering the derivation of meaningful texture information from SAR imagery is the speckle noise. It overlays real structures and causes gray value variations even in homogeneous parts of the image. We propose a filtering technique which can be applied to obtain restored SAR images. Our technique is based on probabilistic methods and regards an image as a random element drawn from a prespecified set of possible images. The despeckle filter can be used as an intermediate step for the extraction of regions of interest, corresponding to structured units in a given area or distinct objects of interest, such as lake-like features on Titan. This tool can therefore be used, among other, to study seasonal surficial changes of Titan’s polar regions. In this study we also present a segmentation technique that allows us to separate the lakes from the local background.
Other Recent work on Probabilistic Despeckle
Abstract
A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.
doi: 10.1109/36.868883
Abstract: Basic textures as they appear, especially in high resolution SAR images, are affected by multiplicative speckle noise and should be preserved by despeckling algorithms. Sharp edges between different regions and strong scatterers also must be preserved. To despeckle images, the authors use a maximum aposteriori (MAP) estimation of the cross section, choosing between different prior models. The proposed approach uses a Gauss Markov random field (GMRF) model for textured areas and allows an adaptive neighborhood system for edge preservation between uniform areas. In order to obtain the best possible texture reconstruction, an expectation maximization algorithm is used to estimate the texture parameters that provide the highest evidence. Borders between homogeneous areas are detected with a stochastic region-growing algorithm, locally determining the neighborhood system of the Gauss Markov prior. Smoothed strong scatterers are found in the ratio image of the data and the filtering result and are replaced in the image. In this way, texture, edges between homogeneous regions, and strong scatterers are well reconstructed and preserved. Additionally, the estimated model parameters can be used for further image interpretation methodsURL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=868883&isnumber=18811
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