Current speckle filters attempt to restore the radar reflectivity using only the multiplicative speckle noise assumption. The best known filters, namely the Frost, Lee or Kuan niters are adaptive filters based on the local statistics, computed in a fixed square window. In this way, the speckle is reduced as a function of the heterogeneity measured by the local coefficient of variation. When the radar reflectivity undergoes significant variations due to the presence of strong scatterers or structural features (edges or lines) in the processing window, such speckle filtering is less effective. In this paper it is shown that the filtering process can be controlled both by the coefficient of variation and by various geometrical ratio detectors. Through shape adaptive windowing, these detectors allow the use of large window sizes for better speckle reduction while preserving spatial resolution and structural features. The backscattered intensity is modelled as K-distributed within speckled targets and...

Structure detection and statistical adaptive speckle filtering in SAR images

In this work, spectral estimation techniques are used to exploit baseline diversity of a multichannel interferometric synthetic aperture radar (SAR) system and overcome the layover problem. This problem arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the sensed scene. We propose a multilook approach to counteract the presence of multiplicative noise, which is due to the extended nature of natural targets; to this purpose we extend the RELAX algorithm to the multilook data scenario (M-RELAX). A thorough performance analysis of nonparametric (beamforming and Capon) and parametric (root MUSIC and M-RELAX) techniques is carried out based on Monte Carlo simulations and Cramer-Rao lower bounds (CRLB) calculation. The results suggest the superiority of parametric methods over nonparametric ones.

Layover solution in multibaseline SAR interferometry

Several optimal techniques exist to reduce speckle effects on polarimetric data, e.g. the Linear Minimum Mean Square Error (LMMSE) vector filter for multilook detected data or optimum summations such as the Polarimetric Whitening Filter (PWF) for one look complex data. Among other drawbacks, these standard methods do not preserve full polarimetric data, or they do not use the a priori texture distribution, or they are restricted to one look data. In the simplified case of data satisfying the so-called "product model", new optimal techniques are described in this paper that are able to reduce speckle effects on multilook data, while preserving fully polarimetric information and texture variations. This "product model" is valid when the scene texture has a large scale of variation and is polarization independent, for instance in K-distributed clutter. Under this assumption, the measured covariance matrix (multilook data) is the product of a scalar random variable /spl mu/ (the texture) and the covariance matrix C/sub zh/ of an equivalent Gaussian homogeneous surface. C/sub zh/ is the mean covariance matrix and contains the polarimetric information. A PWF for multilook complex data (MPWF) is proposed and is shown to be related to optimal statistical estimators of the texture (Maximum Likelihood, Maximum A Posteriori, MMSE...) when the complex Wishart distribution is used. The ML estimation of C/sub zh/ for textured areas is given and the adaptive filters based on these new tools are described. The results indicate a large speckle reduction. Moreover, the mean values of polarimetric features such as the magnitude and the phase of the HH-VV complex degree of coherence are preserved.

Optimal speckle reduction for the product model in multilook polarimetric SAR imagery and the Wishart distribution

Synthetic aperture radar (SAR) data with polarization diversity and multibaseline acquisition has the capability to resolve the structural information of the target. Three-dimensional (3-D) imaging using SAR tomography is an advance and recent technology that focuses on backscattered echoes not only in the 2-D plane defined by the azimuth and range coordinates, but also in cross-range direction. Forests act as a semitransparent media for radar wavelengths, and thus allow us to gather the vertical distribution of this volumetric scatterer. This paper investigated the potential of polarimetric SAR tomography (PolTomSAR) to retrieve the scatterer distribution of forest area using C-band Radarsat-2 spaceborne SAR dataset. Teak patch of Haldwani forest was chosen as the test site to validate the robustness of TomoSAR algorithms. Fourier, beamforming, Capon, Capon like, and polarimetric capon were implemented in this study and backscatter power variations with their corresponding vertical profile for a particular azimuth bin were derived and analyzed. It was observed that Fourier suffered from high sidelobes that were reduced by beamforming and Capon. Limitations of beamformer were highlighted and well resolved by Capon. However, both beamforming and Capon yielded low backscattering power at different height levels. The Capon-like algorithm substantially improved the backscatter intensity at different forest height levels. Polarimetric capon surpassed the Capon algorithm and was able to estimate better heights by incorporating various polarization channels. Height map was generated for fully polarimetric Capon and validated with the ground truth data.

Spaceborne PolSAR Tomography for Forest Height Retrieval

Speckle phenomenon in synthetic aperture radar (SAR) images makes their visual and automatic interpretation a difficult task. To reduce strong fluctuations due to speckle, total variation (TV) regularization has been proposed by several authors to smooth out noise without blurring edges. A specificity of SAR images is the presence of strong scatterers having a radiometry several orders of magnitude larger than their surrounding region. These scatterers, especially present in urban areas, limit the effectiveness of TV regularization as they break the assumption of an image made of regions of constant radiometry. To overcome this limitation, we propose in this paper an image decomposition approach. There exist numerous methods to decompose an image into several components, notably to separate textural and geometrical information. These decomposition models are generally recast as energy minimization problems involving a different penalty term for each of the components. In this framework, we propose an energy suitable for the decomposition of SAR images into speckle, a smooth background, and strong scatterers, and discuss its minimization using max-flow/min-cut algorithms. We make the connection between the minimization problem considered, involving the L0 pseudonorm, and the generalized likelihood ratio test used in detection theory. The proposed decomposition jointly performs the detection of strong scatterers and the estimation of the background radiometry. Given the increasing availability of time series of SAR images, we consider the decomposition of a whole time series. New change detection methods can be based on the temporal analysis of the components obtained from our decomposition.

/pdf/multitemporal-sar-image-decomposition-into-strong-scatterers-3wr7qomj6l.pdf

Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

As the output of the SAR data processor is a complex number, speckle reduction should take profit of the additional information contained in the phase and in the complex correlation between the different images. In order to obtain high equivalent number of looks, we develop the Complex Gaussian-Gamma MAP (Maximum A Posteriori) fdter for complex multiplicative speckle noise and for K-distributed data in the case of L separate correlated complex looks data. This filter is a combination of the usual incoherent multilook processing (Doppler spectral domain) and of an adaptive spatial averaging (image spatial domain). The solution of the filter equation is very easy to handle and is the rigorous solution of the correlated looks MAP fdter.

https://ieeexplore.ieee.org/iel2/1014/12511/00578288.pdf

G.A. Solaas

Papers

Adaptive Processing of Multilook Complex Sar Images