Two-dimensional adaptive block Kalman filtering of SAR imagery

A method for removing speckle from synthetic aperture radar (SAR) imagery by using 2-D adaptive block Kalman filtering is introduced. The image process is represented by an autoregressive model with a nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which taken into account not only the effect of speckles as multiplicative noise but also the effects of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as those of the additive noise     

Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery

Radar images can show great variability from pixel to pixel, which is an obstacle to effective processing. This variability, due to speckle created by the radar wave coherence, necessitates the use of more adapted filters. Previous studies have shown that multiresolution wavelet analysis yields better results but produces artefacts due to multiscale decomposition. This paper proposes a method that reduces these effects by introducing the fractal dimension. The resultant filter combines wavelet decomposition and variance change model based on the level of variance estimated by studying the fractal dimension of the image.     

The principle of speckle filtering in polarimetric SAR imagery

The principle of speckle reduction in polarimetry is reconsidered. It is shown that polarimetric data can be speckle reduced if and only if all the elements of the Mueller matrix are filtered, which is equivalent to filtering the scattering vector covariance matrix. Assuming that speckle is multiplicative and stationary, the algorithms proposed by S.L.Lee et al. (1991) and S.Goze et al. (1993) are extended to filter the covariance matrix of reciprocal and nonreciprocal targets on one-look and multilook images. The problem of estimation of the first- and second-order statistics of the four-channel speckle vector is discussed, and a solution is proposed for one-look and multilook images     

Sidelobe reduction in reflective s.a.w. pulse compressors without external filtering

The letter describes the incorporation of amplitude weighting into the response characteristics of a reflective-array compressor. The reflective-array grooves, formed by ion etching, have nonuniform depth, thereby achieving a nonuniform amplitude response. The resulting sidelobe reduction is then evaluated, and the advantages of this weighting method over external and other internal weighting methods are discussed.     

Speckle reduction in multipolarization, multifrequency SAR imagery

An algorithm to take advantage of this polarization diversity to suppress the speckle effect with much less resolution broadening than using spatial filtering is discussed. The coupling between polarization channels is minimized by using local intensity ratios. The degree of speckle reduction is similar to two-look or three-look processing. The same algorithm can also be used to process multifrequency polarimetric SAR. For three-frequency aircraft SAR data speckle reduction equivalent to six-look processing can be achieved. Further speckle reduction is possible by applying speckle filters in the spatial domain. In addition, a vector speckle filter which operates simultaneously in the polarization and spatial domains is tested. Experimental results with simulated polarimetric SAR as well as one-look and multilook parametric SAR data demonstrate the effectiveness of these speckle reductions, with minimum resolution broadening and coupling between polarimetric and frequency channels. Comparisons with other algorithms are also made     

Complex SAR imagery and speckle filtering for wave imaging

A method is described to predict the wavenumber dependence of the speckle component in spectra of synthetic aperture radar intensity images. Filtering of this component is an important step in recovering waveheight spectra for synthetic-aperture radar (SAR) images of the ocean, and an effective means of doing so is required for the 'wave mode' of the European satellite ERS-1. The method uses the correlation function of the corresponding complex images and has been tested using a variety of airborne and spaceborne imagery obtained over both land and sea. Examples are shown of both successful and unsuccessful applications of the method.<>     

SAR image despeckling via bilateral filtering

Bilateral filtering (BF) can realise both smoothing images and preserving edges, whereas its filtering results are always influenced since its two parameters are difficult to configure to the optimum. In this reported work, the application of BF is extended to synthetic aperture radar (SAR) image despeckling, and the despeckling evaluation indexes, including the equivalent number of looks and the edge save index, are used to estimate the parameters. After BF with estimated parameters imposed on a normalised SAR image, further processing can achieve both despeckling and edge preservation simultaneously. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform the classical Lee filtering.     

Low-power filtering via adaptive error-cancellation

A low-power technique for digital filtering referred to as adaptive error-cancellation (AEC) is presented in this paper. The AEC technique falls under the general class of algorithmic noise-tolerance (ANT) techniques proposed earlier for combating transient/soft errors. The proposed AEC technique exploits the correlation between the input and soft errors to estimate and cancel out the latter. In this paper, we apply AEC along with voltage overscaling (VOS), where the voltage is scaled beyond the minimum (referred to as V/sub dd-crit/) necessary for correct operation. We employ the AEC technique in the context of a frequency-division multiplexed (FDM) communication system and demonstrate that up to 71% energy reduction can be achieved over present-day voltage-scaled systems.     

Delayed N-path structure for high speed adaptive FIR digital filtering

A structure and its algorithm for high speed adaptive FIR digital filtering based on the delayed N-path concept is presented. Using N/sup 2/ processors, the throughput rate of the proposed filter can be N/sup 2/ times faster than that of the same filter realised directly using one such processor.<>     

一种新的自适应小波阈值SAR图像滤波算法

为有效降低乘性斑点噪声对合成孔径雷达（SAR）图像的影响,提出了一种新的基于小波系数广义高斯分布（GGD）模型的自适应阈值估计去噪算法。首先分析了经对数变换的SAR图像小波系数的统计分布特性,然后提出了子带自适应阈值估计方法,通过对数变换,将该算法应用于含斑点噪声的SAR图像去噪。仿真图像和真实SAR图像的实验结果表明,该算法同目前流行的其他阈值算法相比,运算复杂度低,算法高效,并且在保留原始图像重要细节特征和图像后向散射特性的同时,显著地减少相干斑噪声。     

Speckle filtering of SAR images based on adaptive windowing

Speckle noise usually occurs in synthetic aperture radar (SAR) images owing to coherent processing of SAR data. The most well-known image domain speckle filters are the adaptive filters using local statistics such as the mean and standard deviation. The local statistics filters adapt the filter coefficients based on data within a fixed running window. In these schemes, depending on the window size, there exists trade-off between the extent of speckle noise suppression and the capability of preserving fine details. The authors propose a new adaptive windowing algorithm for speckle noise suppression which solves the problem of window size associated with the local statistics adaptive filters. In the algorithm, the window size is automatically adjusted depending on regional characteristics to suppress speckle noise as much as possible while preserving fine details. Speckle noise suppression gets stronger in homogeneous regions as the window size increases succeedingly. In fine detail regions, by reducing the window size successively, edges and textures are preserved. The fixed-window filtering schemes and the proposed one are applied to both a simulated SAR image and an ERS-1 SAR image to demonstrate the excellent performance of the proposed adaptive windowing algorithm for speckle noise     

HRLS: a more efficient RLS algorithm for adaptive FIR filtering

The fast convergence rate and its immunity to the eigenvalue spread of the input correlation matrix make the RLS algorithm particularly attractive. However, the computational complexity is high. We propose using a hierarchical approach to reduce the computational complexity and further increase the convergence rate. The results of simulation runs and theoretical justifications confirm our claims     

IIR adaptive filtering via discrete Legendre functions

A new IIR adaptive filter structure is proposed. It is based on modelling the impulse response of the system to be identified as a linear combination of a set of discrete orthogonal functions. Computer simulations show that the proposed structure has better convergence performance than FIR transversal filters, when they are required to identify systems with long impulse response. The comparison is made on the basis that both of them have the same number of multipliers     

A new two-dimensional block adaptive FIR filtering algorithm andits application to image restoration

This paper presents a new two-dimensional (2-D) optimum block stochastic gradient (TDOBSG) algorithm for 2-D adaptive finite impulse response (FIR) filtering. The TDOBSG algorithm employs a space-varying convergence factor for all the filter coefficients, where the convergence factor at each block iteration is optimized in a least squares sense that the squared norm of the a posteriori estimation error vector is minimized. It has the same order of computational complexity as another 2-D optimum block adaptive (TDOBA) algorithm. Computer simulations for image restoration show that the TDOBSG algorithm outperforms the TDOBA algorithm and other related algorithms in terms of objective and/or subjective measures.     

激光主动探测图像散斑自适应滤波抑制方法研究

激光主动照明成像具有作用距离远、系统分辨率高、可在低照度背景等复杂环境下获取目标图像等优点,但探测图像会受散斑噪声干扰.把高斯滤波、均值滤波和自适应滤波方法分别应用到仿真实验中进行散斑噪声抑制,实验表明:与高斯滤波和均值滤波相比,自适应滤波能有效抑制图像噪声,保留图像的边缘和细节信息.利用自适应滤波方法对获取的单帧和多帧累加平均的激光主动探测图像进行散斑抑制实验,使用散斑对比度进行定量分析,结果表明多帧短曝光图像累加平均可有效抑制图像的散斑噪声,自适应滤波可进一步降低图像的散斑噪声.     

Sidelobe reduction in array-pattern synthesis using geneticalgorithm

A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays     

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

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 μ (the texture) and the covariance matrix C_zh of an equivalent Gaussian homogeneous surface. C_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 _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     

Nonparametric spectral analysis of gapped data via an adaptive filtering approach

We present an algorithm for nonparametric complex spectral analysis of gapped data via an adaptive finite impulse response (FIR) filtering approach, referred to as the gapped-data amplitude and phase estimation (GAPES) algorithm. The incomplete data sequence may contain gaps of various sizes. The GAPES algorithm iterates the following two steps: (1) estimating the adaptive FIR filter and the corresponding complex spectrum via amplitude and phase estimation (APES), a nonparametric adaptive FIR filtering approach, and (2) filling in the gaps via a least-squares APES fitting criterion. The initial condition for the iteration is obtained from the available data segments via APES. Numerical results are presented to demonstrate the effectiveness of the proposed GAPES algorithm.This work was supported in part by the Senior Individual Grant Program of the Swedish Foundation for Strategic Research, AFRL/SNAT, Air Force Research Laboratory, Air Force Material Command, USAF, under grant number F33615-99-1-1507, and the National Science Foundation Grant MIP-9457388. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notice thereon.     

Local image registration by adaptive filtering.

We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion.     

Non-Gaussian signal statistics in ocean SAR imagery

The authors have studied the significance of non-Gaussian signal statistics in some synthetic aperture radar (SAR) images of the ocean surface. The study consisted of calculating the amplitude histogram of the returned echoes from the images and comparing these with the Rayleigh- and Kν-distributions, corresponding to the Gaussian and non-Gaussian statistics, respectively. The images used were some C-band SAR data from the Canadian airborne SAR collected during the NORCSEX'88 campaign and some ERS-1 data collected during the NORCSEX'91 campaign. The analysis of the NORCSEX'88 data included studies of the dependency of the signal statistics on incidence angle and meteorological and imaging conditions. It was found, specifically at small incidence angles, that there was a significant deviation from Gaussian statistics. It was also found that when the wind was blowing against the waves, the deviation from Gaussian statistics was more pronounced than when the wind was blowing in the same direction as the waves were propagating. The study also showed a correlation between the signal statistics and the width of the SAR image spectra. At low incidence angles, this agrees with the interpretation that non-Gaussian statistics may be related to strong widebanded scattering events. However, since non-Gaussian statistics also were observed at incidence angles as high as 50°, it is evident that the modulation of the scattering cross section by the long waves is also an important factor. In addition, the analysis of the ERS-1 data showed that to account for the width of the SAR image spectra, an azimuth smearing term, due to short scene coherence time, had to be included. This was in the present work done by modeling the short-coherence-time-smearing as a Gaussian low-pass filter. By this procedure, the authors were able to obtain realistic estimates for the average scene coherence time of the SAR scenes