A Noncentral and Non-Gaussian Probability Model for SAR Data (original) (raw)
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Modeling non-Rayleigh speckle distribution in SAR images
IEEE Transactions on Geoscience and Remote Sensing, 2002
In non-Rayleigh distributed radar images, the number of scatterers can be viewed as a Poisson distributed random variable, with the mean itself random. When this mean is Gamma distributed, then the image classically satisfies the distribution. We add three new possible distributions for this mean: inverse Gamma, Beta of the first kind, and Beta of the second kind. We show that new intensity distributions so obtained can be estimated, with the interest of the extension validated on a real image.
An overview of speckle noise filtering in SAR images
… , First Latino-American Seminar on Radar …, 1997
2. A Statistical Model for Speckle Synthetic Aperture Radar Images are corrupted by a signal-dependent noise called speckle, that decreases the potentiality of these images for human or automatic interpretation. This degradation is due to the coherent nature of the radiation that is used and its interaction with the roughness of the terrain. In this paper,a survey of the techniques that have been proposed for reducing speckle noise in a SAR image is presented. First, a statistical model for the the speckle noise is presented. This model, multiplicative in its nature, leads to the majority of the algoritluns for speckle noise reduction. We describe the Lee, Kuan, Frost and adaptive versions of the last three filters, which are the most well known algorithms, as well as the MAP filter and some heuristic filters. We also briefly present new algorithms based on the theories of Markov Random Fields and Robust Estimation.. Finally, we describe techniques for the evaluation of speck1e reduction filters.
On the Extension of Multidimensional Speckle Noise Model From Single-Look to Multilook SAR Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2000
Speckle noise represents one of the major problems when synthetic aperture radar (SAR) data are considered. Despite the fact that speckle is caused by the scattering process itself, it must be considered as a noise source due to the complexity of the scattering process. The presence of speckle makes data interpretation difficult, but it also affects the quantitative retrieval of physical parameters. In the case of one-dimensional SAR systems, speckle is completely determined by a multiplicative noise component. Nevertheless, for multidimensional SAR systems, speckle results from the combination of multiplicative and additive noise components. This model has been first developed for single-look data. The objective of this paper is to extend the single-look data model to define a multilook multidimensional speckle noise model. The asymptotic analysis of this extension, for a large number of averaged samples, is also considered to assess the model properties. Details and validation of the multilook multidimensional speckle noise model are provided both theoretically and by means of experimental SAR data acquired by the experimental synthetic aperture radar system, operated by the German Aerospace Center.
Speckle analysis and smoothing of synthetic aperture radar images
Computer graphics and image processing, 1981
Coherent processing of synthetic aperture radar (SAR) data makes images susceptible to speckles. Basically, the speckles are signal-dependent and, therefore, act like multiplicative noise. This paper develops a statistical technique to define a noise model, and then successfully applies a local statistics noise filtering algorithm to a set of actual SEASAT SAP, images. The smoothed images permit observers to resolve fine detail with an enhanced edge effect. Several SEASAT SAR images are used for demonstration.
Speckle Suppression in SAR Images Using the 2-D GARCH Model
IEEE Transactions on Image Processing, 2000
A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar (SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.
2004
One of the major factors plaguing the performance of synthetic aperture radar (SAR) imagery is the signal-dependent, speckle noise. Grainy in appearance, it is due to the phase fluctuations of the electromagnetic returnedsignals. Since the inherent spatial-correlation characteristics of speckle in SAR images are not embedded in themultiplicative models for speckle noise, a new approach is proposed here that provides a new mathematicalframework for modeling and mitigation of speckle noise. The contribution of this report is thus twofold. First, anovel model for speckled SAR imaging is introduced based on Markov random fields (MRFs) in conjunction withstatistical optics. Second, utilizing the model, a global energy-minimization algorithm, the simulated annealing(SA), is introduced for speckle reduction. In particular, the joint conditional probability density function (cpdf)of the intensity of any two points in the speckled image and the associated correlation function are used to deriv...
A Physically Consistent Speckle Model for Marine SLC SAR Images
A new physically based speckle model for marine single-look complex (SLC) synthetic aperture radar (SAR) images is here presented and investigated. The model allows using full-resolution SAR images instead of multilook SAR images, in which, at the expense of a coarser spatial resolution, the speckle is mitigated. The model is based on the three-parameters generalized-(GK) probability density function (pdf). GK pdf is a suitable physically-based speckle model for marine SAR images ensuring a continuous and physically consistent transition among different scattering scenarios. This speckle model embodies Rayleigh, , and Rice scattering scenes which are typical of marine scenes. The use of the three parameters, related to the GK pdf ones, is able to highlight the presence of both low backscattering areas and areas in which a small dominant scatterer is present. This is operationally interesting in SAR oil spill detection procedures.
Formulation and validation of a multidimensional SAR data speckle noise model
2004
The availability of multidimensional Synthetic Aperture Radar (SAR) imagery makes possible the physical characterization of scatterers through the inversion of electromagnetic scattering models. Natural scatterers need to be characterized stochastically as a consequence of the coherent nature of SAR systems, leading to speckle. At the resolution at which SAR systems operate, speckle turns out to be a noise source. Consequently, a complete characterization of this noise source, specially for correlated multidimensional SAR imagery is crucial for a correct information interpretation from a quantitative point of view. In this direction, this paper presents a novel multidimensional speckle noise model, in a matrix-based formulation, as well as, its validation by means of real interferometric and polarimetric SAR data.
The effect of speckle filtering on SAR texture discrimination
Simpósio Brasileiro de …, 1996
In tropical ecology studies, forest classification is a key issue.Although there is no widely accepted forest classification criterion, it is recognized that texture is an important factor to discriminate forest types and other land cover, particularly when using radar images. Synthetic Aperture Radar (SARA) images, however, are contaminated by a multiplicative noise, known as speckle, wich disturbs the texture identification. Several filters have been proposed to atenuate this kind of noise, but the effect of these filters on texture is not well known. In this paper, textures are modelled by two-dimensional autorregressive (AR-2D) models. These models are estimated for each one of the samples of SAR textures before and after speckle filtering. Eight samples of primary forest and seven samples of non-forest (pasture and agricultural crops) were collect from SAREX data (Cband, HH polarization, 6m resolution, 6 looks) in the Tapajós National Forest (Flona) region in Pará state, Brazil. All these samples were filtered by a 3 x 3 Box filter and a 3 x 3 Frost filter. Euclidean distances were computed between the model coefficient vector of the samples and the average coefficient vector for the two classes (defined here as the class vectors) for the unfiltered and filtered cases separately. For all cases the coefficient vectors formed two separated clusters, corresponding to each one of the classes, in a non-linear mapping of the coefficient space. The conclusions are: AR modelling is an effective method to idendify and discriminate radar texture. The discriminatory power, however, is higher using the unfiltered channels than when the simple Box and the Frost filter are used.