Statistical Distribution And Texture In Multilook And Complex Sar Images

Lopès, A.; Laur, Henri; Nezry, E.

doi:10.1109/igarss.1990.689030

Cited by 30 publications

(14 citation statements)

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“…Note that assuming a Gamma-distributed R, and by performing the integration in Equation (28), the pdf P(I)=P(I/R) of the intensity is a K-distribution [57]. Introduced in 1976 [58] by British researchers of the RSRE (later DRA) to describe the non-Gaussian properties of waves backscattered by objects within a radar resolution cell, the K-distribution has been theoretically recognised as the pdf of the intensity I backscattered by a rough non-stationary surface [61] such as most natural scenes observed by a radar.…”

Section: Gamma Distributed Imaged Scenementioning

confidence: 99%

Adaptive Speckle Filtering in Radar Imagery

Nezry¹

2014

Land Applications of Radar Remote Sensing

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" . Since , the team led by Dr. "rmand Lopès at the Centre d'Etude Spatiale des Rayonnements in Toulouse France , has carried out and then inspired the development of the most efficient speckle filters existing today. Since , with speckle filters having reached a satisfactory level of performance, no significant advances have been made. Nevertheless, in this period, the use of speckle filters in a wide range of applications using S"R imagery has become generalized." radar wave can be considered, with a good approximation, as plane, coherent and monochromatic. It is emitted by an antenna towards a target. The target backscatters partially the radar wave in the direction of a receiving antenna. In the vast majority of spaceborne Synthetic "perture Radars S"R , a single antenna assumes the two functions of emission and reception monostatic radar .The complete radar measurement is the combination of the horizontally H and vertically V linearly polarised radar waves, at emission and at reception after backscattering by the observed target. "fter signal calibration, this measurement, affected by noise, enables to restitute for each resolution cell a polarimetric backscattering matrix S This detected signal intensity I is proportional in average to the radar "backscattering coefficient" σ°. The backscattering coefficient σ°= .π.|S pq | is the average radar cross-section per surface unit [ ]. σ°, expressed in m /m , is a dimensionless physical quantity. It is a physical property of the sensed surface, which depends principally on its roughness, its dielectric properties, its geometry, and the arrangement of its individual scatterers.Carrying the radiometric information with regard to the sensed target, σ° is a function of the frequency of the radar wave, of its angle of incidence upon the target, and of the configuration of polarisation. In terms of physical meaning, the radar backscattering coefficient is analogous to the bidirectional reflectance in the domain of optical wavelengths σ° # cos θ, where is the incidence angle of illumination on the sensed target.Nevertheless, detected radar images look visually very noisy, exhibiting a very characteristic salt-and-pepper appearance with strong tonal variations from a pixel to the next. Indeed, since radar imaging systems are time-coherent, radar measurements over random rough surfaces are corrupted by "speckle" noise due to the random modulation of waves reflected by the elementary scatterers randomly located in the resolution cell. Then, coherent summation of the phases of elementary scatterers within the resolution cell results in a random phase of the complex pixel value.This speckle "noise" makes both photo-interpretation and the estimation of σ° extremely difficult. "ctually, speckle is a physical phenomenon, which is inherent to all coherent imaging systems radar, lidar, sonar, echography . In most remote sensing applications using radar/S"R imagery, speckle is generally considered a very strong noise that must be energically filtered to obtain an image on which cl...

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Section: Gamma Distributed Imaged Scenementioning

confidence: 99%

Adaptive Speckle Filtering in Radar Imagery

Nezry¹

2014

Land Applications of Radar Remote Sensing

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“…These properties might be used for the development of tools for SAR image processing and analysis, like filters for speckle noise reduction, segmentation and classification algorithms, among others. There are plenty of statistical results regarding univariate intensity and amplitude SAR imagery, but less is known about multivariate multi-channel polarimetric and interferometric multi-look information (see, for instance: Frery et al, 1997;Jakeman and Pusey, 1976;Kuttikkad and Chellappa, 2000;Lopes et al, 1990;Lee et al, 1995;Lee et al, 1994a, Lee et al, 1994bLee et al, 1994c;Yueh et al, 1989;Yueh et al, 1991).…”

Section: Introductionmentioning

confidence: 97%

The polarimetric 𝒢 distribution for SAR data analysis

2004

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SUMMARYRemote sensing data, and radar data in particular, have become an essential tool for environmental studies. Many airborne polarimetric sensors are currently operational, and many more will be available in the near future including spaceborne platforms. The signal-to-noise ratio of this kind of imagery is lower than that of optical information, thus requiring a careful statistical modelling. This modelling may lead to useful or useless techniques for image processing and analysis, according to the agreement between the data and their assumed properties. Several distributions have been used to describe synthetic aperture radar (SAR) data. Many of these univariate laws arise by assuming the multiplicative model, such as Rayleigh, square root of gamma, exponential, gamma, and the class of K I distributions. The adequacy of these distributions depends on the detection (amplitude, intensity, complex, etc.), the number of looks, and the homogeneity of the data. In Frery et al. (1997), another class of univariate distributions, called G, was proposed to model extremely heterogeneous clutter, such as urban areas, as well as other types of clutter. This article extends the univariate G family to the multivariate multi-look polarimetric situation: the G p law. The new family has the classical polarimetric multi-look K p distribution as a particular case, but another special case is shown to be more flexible and tractable, while having the same number of parameters and fully retaining their interpretability: the G 0 p law. The main properties of this new multivariate distribution are shown. Some results of modelling polarimetric data using the G 0 p distribution are presented for two airborne polarimetric systems and a variety of targets, showing its expressiveness beyond classical models.

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“…The algorithm has been successfully applied to many image segmentation fields. Furthermore, considering the speckle noise in SAR image, Gamma distribution is often adopted to depict the stochastic characteristic of pixels (Lopes et al, 1990). Although Dong et al (1998) have proved that Gamma distribution is more applicable for SAR image than Gaussian distribution, the histogram distribution in one cluster usually tends to have multimodal characteristics which are difficult to be fitted by one gamma distribution (Peng et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Regional Sar Image Segmentation Based on Fuzzy Clustering With Gamma Mixture Model

Zhao

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

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Statistical Distribution And Texture In Multilook And Complex Sar Images

Cited by 30 publications

References 8 publications

Adaptive Speckle Filtering in Radar Imagery

Adaptive Speckle Filtering in Radar Imagery

The polarimetric 𝒢 distribution for SAR data analysis

Regional Sar Image Segmentation Based on Fuzzy Clustering With Gamma Mixture Model

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