Synthetic Aperture Radar Speckle Research Articles

Supervised Constrained Optimization of Bayesian Nonlocal Means Filter With Sigma Preselection for Despeckling SAR Images

Speckle reduction is an important problem in synthetic aperture radar (SAR) image analysis. Recent years have seen how Bayesian filters emerge as the natural extension of the nonlocal means filters, providing a general framework to deal with multiplicative (speckle) noise. In this paper, we present an easy-to-use software tool applying an evolutionary algorithm to optimize a Bayesian nonlocal means filter with sigma preselection for denoising SAR images. The desired result is a filtered image having a significative reduction in its variance but preserving the original mean value of the noisy image. A mixed-integer constrained optimization problem is stated and solved with the human intervention, where the user assists the evolutionary algorithm to reduce the noisy image variance under the restriction of keeping the mean value of the noisy SAR image within a predetermined interval of acceptance. We apply the methodology to a set of synthetic and real SAR speckle corrupted images. The results through the evaluation of objective global and local quality criteria show the excellent potential of the proposal.

Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm

This paper presents an algorithm dealing with initial segmentation of speckled Synthetic Aperture Radar (SAR) intensity images in order to automatically determine the number of homogeneous regions. Taking this problem into account, segmentation procedure utilizing splitting and merging is designed, iteratively. The proposed approach is based upon Bayesian inference, a maximum likelihood gamma distribution parameter estimator, and a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using of image splitting operation, SAR image is partitioned into finite regions iteratively, until all individual regions are coherent. Then each region is assigned a unique label to indicate the class to which the homogeneous region belongs. The intensities of pixels in each coherent region are assumed to satisfy identical and independent gamma distribution. Then an RJMCMC scheme is designed to simulate the posterior distribution in order to estimate the number of components and delineate an initial segmentation. Thus, the main purpose of this research is to define the number of homogeneous regions rather than a perfect segmentation, i.e. model outputs can be served for unsupervised segmentation methodologies as prior information. The results obtained from Radarsat-1/2 of SAR intensity images show that the proposed algorithm is both capable and reliable in defining the accurate number of homogeneous regions in a wide variety of SAR intensity images, comprising a high level of speckle noise.

Novel polarimetric SAR speckle filtering algorithm based on mean shift

For better interpretation of synthetic aperture radar (SAR) images, the speckle filtering is an important issue. In the area of speckle filtering, the proper averaging of samples with similar scattering characteristics is of great importance. However, existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment. A novel polarimetric SAR (PoISAR) speckle filtering algorithm based on the mean shift theory is proposed. As polarimetric covariance matrices or coherency matrices form Riemannian manifold, the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace. By using the range-spatial joint mean shift theory in Riemannian manifold, the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain. German Aerospace Center (DLR) L-Band Experiment SAR (E-SAR) data and East China Research Institute of Electronic Engineering (ECRIEE) PolSAR data are used to demonstrate the efficiency of the proposed algorithm. The filtering results of two commonly used speckle filtering algorithms, refined Lee filtering algorithm and intensity driven adaptive neighborhood (IDAN) filtering algorithm, are also presented for the comparison purpose. Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering, edge protection as well as polarimetric characteristics preservation.

A New Method of Tank Detection for SAR Images

The tank is one of important military targets. Tanks detection is the study focus of the synthetic aperture radar(SAR) image processing currently. But there may be many false alarms existed in the detection result with most of the traditional tank detection methods affected by the SAR speckle. A new method of tank detection for SAR images based on the features of SAR images is put forward by this paper. It uses Gauss low-pass filtering to smooth the original image and the geometric active contour model based on prediction theory metric to realize automatic segmentation. It detects candidate targets by removing little connected regions. Finally, it further removes the false alarm targets based on the grey characteristic of the shadow regions. Experimental results indicate that the method can effectively and rightly detect tanks of SAR images. Moreover, it is insensitive to the initial contour.

Edge-detection-based filter for SAR speckle noise reduction

Some adaptive filters, such as the Kuan, Lee, minimum mean square error (MMSE) and Frost filters, have been tested on synthetic aperture radar (SAR) data without considering the level of homogeneity in the pixels. Therefore, they degrade the spatial resolution of images and smooth details, while also decreasing the speckle noise level. There are other filters, such as the enhanced Lee and gamma maximum a posteriori (MAP), that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. In addition to these weaknesses, pixels surrounding a point scatterer are also treated as point scatterers due to inadequacy of the method based on evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the assessment of similarity of homogeneity levels in the image, incorporating edge-detection filters to identify meaningful features and an algorithm to filter the pixels surrounding point scatterers. This method, called the UNSW (University of New South Wales) adaptive filter (UAF), was compared to nine filters using different quantitative and qualitative methods. The results show the ability of the UAF to simultaneously reduce speckle and preserve details as well as its ability to filter more pixels. The effect of increasing the damping factor on speckle noise reduction performance has also been assessed using this method.

Training neural networks on artificially generated data: a novel approach to SAR speckle removal

A neural network-based method for speckle removal in synthetic aperture radar (SAR) images is introduced. The method rests on the idea that a neural network learning machine, trained on artificially generated input–target couples, can be used to efficiently process real SAR data. The explicit plus-point of the method is that it is trained with artificially generated data, reducing the demands put on real input data such as data quality, availability and cost price. The artificial data can be generated in such a way that they fit the particular characteristics of the images to be denoised, yielding case-specific, high-performing despeckling filters. A comparative study with three classical denoising techniques (Enhanced Frost (EF), Enhanced Lee (EL) and Gamma MAP (GM)) and a wavelet filter demonstrated a superior speckle removal performance of the proposed method in terms of quantitative performance measures. Moreover, qualitative evaluation of the despeckled results was in favour of the proposed method, confirming its speckle removal efficiency.

Two Novel Bayesian Multiscale Approaches for Speckle Suppression in SAR Images

Speckle suppression is a prerequisite for many synthetic aperture radar (SAR) image-processing tasks. Previously, we introduced a Bayesian-based speckle-suppression method that employed the 2-D generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for wavelet coefficients of log-transformed SAR images. Based on this method, we propose two new Bayesian speckle-suppression approaches in this paper. In the first approach, we introduce a new heteroscedastic model, i.e., the 2D-GARCH Mixture (2D-GARCH-M) model, as an extension of the 2D-GARCH model. This new model can capture the characteristics of wavelet coefficients. Also, the 2D-GARCH-M model introduces additional flexibility in the model formulation in comparison with the 2D-GARCH model, which results in better characterization of SAR image subbands and improved restoration in noisy environments. Then, we design a Bayesian estimator for estimating the clean-image wavelet coefficients based on 2D-GARCH-M modeling. In the second approach, the logarithm of an image is analyzed by means of the curvelet transform instead of wavelet transform. Then, we study the statistical properties of curvelet coefficients, and we demonstrate that the 2D-GARCH model can capture the characteristics of curvelet coefficients, such as heavy tailed marginal distribution, and the dependences among them. Consequently, under the 2D-GARCH model, we design a Bayesian estimator for estimating the clean-image curvelet coefficients. Finally, we compare these methods with other denoising methods applied on artificially speckled and actual SAR images, and we verify the performance improvement in utilizing the new strategies.

SAR image heterogeneity and speckle filtering

The ratio of the arithmetic to the geometric mean, a general index to test synthetic aperture radar (SAR) image homogeneity, is applied to the speckle reducing algorithm. A novel filter, based on improved speckle reducing anisotropic diffusion, is designed to denoise the SAR image. Experiments validate the effectiveness of the proposed method.

Speckle reduction algorithm for synthetic aperture radar images based on Bayesian maximum <italic>a posteriori</italic> estimation in wavelet domain

The inherent speckle noise in synthetic aperture radar (SAR) images severely degrades the image interpretation and affects the follow-up image-processing tasks. Thus, speckle suppression is a critical step in SAR image preprocessing. We propose a novel locally adaptive speckle reduction algorithm based on Bayesian maximum a posteriori (MAP) estimation in wavelet domain. First, the presented method performs logarithmical transform to original speckled SAR image and an undecimated wavelet transform (UWT). The Rayleigh distribution is used to model the statistics of the speckle wavelet coefficients, and the Laplacian distribution models the statistics of wavelet coefficients due to signal. A Bayesian estimator with a closed-form solution is derived from MAP criterion estimation, and the resulting formula is proved to be equivalent to soft thresholding in nature, which makes our algorithm very simple. Furthermore, the parameters of the Laplacian model are estimated from the coefficients in a neighboring window, thus making the presented method spatially adaptive in the wavelet domain. Theoretical analysis and simulation experiment results show that the proposed method is simple and effective. It significantly improves the visual quality of the SAR images and yields better performance than spatial filterings and traditional wavelet despeckling algorithms.

Radar remote sensing for the delineation of forest cover maps and the detection of deforestation

Summary This paper assesses the feasibility of forest cover mapping and the delineation of deforestation using Japanese Earth Resource Satellite (JERS)-1 Synthetic Aperture Radar (SAR) data. The assessment was carried out at fi ve test sites in Germany (Thuringia), UK (Kielder), Sweden (Remningstorp and Brattaker) and Russia (Chunsky). These temperate and boreal sites all have high forest cover, but with different forestry management practices. The stands at the Swedish, Russian and UK sites are harvested by clear-cutting, while in Thuringia, thinning is the predominant practice. Man-made deforestation is characterized in SAR imagery by regular geometric patterns which can be segmented and classifi ed for data analysis. This reduces the statistical effects of SAR speckle. The procedure for mapping deforested areas exploits time series of SAR images, taken from the period 1992 – 1998 during which JERS was operational. Two different approaches were developed. The fi rst detects forest cover separately for each JERS scene, while the second takes all scenes into account simultaneously. Images are classifi ed into forest, non-forest and deforested areas. The overall accuracy of the derived forest cover map is ~ 90 per cent in acreage and ~ 90 per cent for logging.

Scene estimation from speckled synthetic aperture radar imagery: Markov-random-field approach

A novel Markov-random-field model for speckled synthetic aperture radar (SAR) imagery is derived according to the physical, spatial statistical properties of speckle noise in coherent imaging. A convex Gibbs energy function for speckled images is derived and utilized to perform speckle-compensating image estimation. The image estimation is formed by computing the conditional expectation of the noisy image at each pixel given its neighbors, which is further expressed in terms of the derived Gibbs energy function. The efficacy of the proposed technique, in terms of reducing speckle noise while preserving spatial resolution, is studied by using both real and simulated SAR imagery. Using a number of commonly used metrics, the performance of the proposed technique is shown to surpass that of existing speckle-noise-filtering methods such as the Gamma MAP, the modified Lee, and the enhanced Frost.

Classified pixel-based windowing algorithm for polarimetric SAR speckle filtering

Two main purposes of synthetic aperture radar (SAR) speckle filtering are the preservation of the edge sharpness and the suppression of the speckle noise. However, most of the existing filtering techniques suppress the speckle noise while smearing edges. This problem is partly solved by using nonsquare windows, but it can also generate large errors in many applications. A new windowing algorithm that can overcome the defects of the nonsquare window, by discriminating the edges more precisely with a preliminary terrain classification result, is proposed.

Ice-sheet velocity mapping: a combined interferometric and speckle-tracking approach

AbstractThe first and second RADARSAT Antarctic Mapping Missions (AMM-1 and -2) have now acquired interferometric synthetic aperture radar (SAR) overmuch of the ice sheet. The RADARSAT 24 day repeat cycle is nearly ideal for measuring slow ice motion (e.g. <100ma–1), but application of SAR interferometry is limited in faster-moving areas. With a 1day repeat period, ERS-1/-2 tandem SAR data are much better matched to fast motion, but are not always available. Fortunately, several authors have demonstrated the ability to measure velocity in fast-moving areas by tracking SAR speckle from image to image, which works well even in the absence of visible features. While these estimates have intrinsically lower resolution and poorer accuracy than direct phase measurements, they serve well in areas where there are no data suitable for conventional interferometry. This paper describes algorithms I have developed for merging interferometric and speckle-tracking data from multiple swaths to form a single seamless mosaic of velocity. At each point in the mosaic, all the available data are combined to produce estimates of the velocity and the associated error. This technique is demonstrated using RADARSAT data collected over Lambert Glacier, Antarctica, during AMM-1 and -2.

Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data

One of the problems associated with synthetic aperture radar (SAR) data analysis is the presence of random noise or speckle SAR data, being achromatic in nature, which offers very limited scope for the detection and delineation of various terrain features. ERS-1 SAR data for the coastal region of West Bengal, India were processed (a) to suppress the random noise using various filters, (b) to generate the intensity, hue and saturation (IHS) transform from temporal SAR data, and (c) to study the synergism of SAR data with optical sensor data. The results indicate that the Gamma MSP filter with a 5 5 pixel kernel size has been the most efficient in suppressing the noise and concurrently improving the image contrast. The IHS transform of temporal SAR data made it easier to discriminate between various wetland categories. This was also the case with hybrid image generated by the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging and Self-scanning Sensor (LISS-II) data when compared to SAR data alone.

Polarimetric SAR speckle filtering and its implication for classification

This paper proposes a new approach in polarimetric synthetic aperture radar (SAR) speckle filtering. The new approach emphasizes preserving polarimetric properties and statistical correlation between channels, not introducing crosstalk, and not degrading the image quality. In the last decade, speckle reduction of polarimetric SAR imagery has been studied using several different approaches. All of these approaches exploited the degree of statistical independence between linear polarization channels. The preservation of polarimetric properties and statistical characteristics such as correlation between channels were not carefully addressed. To avoid crosstalk, each element of the covariance matrix must be filtered independently. This rules out current methods of polarimetric SAR filtering. To preserve the polarimetric signature, each element of the covariance matrix should be filtered in a way similar to multilook processing by averaging the covariance matrix of neighboring pixels. However, this must be done without the deficiency of smearing the edges, which degrades image quality and corrupts polarimetric properties. The proposed polarimetric SAR filter uses edge-aligned nonsquare windows and applies the local statistics filter. The impact of using this polarimetric speckle filtering on terrain classification is quite dramatic in boosting classification performance. Airborne polarimetric radar images are used for illustration.

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Spatial considerations in SAR speckle consideration

An approach to the simulation of synthetic-aperture-radar (SAR) image products is described that departs from most of the other simulation algorithms in the method of speckle generation. Speckle is prepared corresponding to the combination of the frequency weightings and look sum strategy of the radar processes desired and then multiplied by the source scene data preconditioned by the desired resolution. The method allows output pixel spacings to be specified independently of more fundamental system parameters. Typical results are presented for SIR-B images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Multipolarization SAR Data for Surface Feature Delineation and Forest Vegetation Characterization

This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of ratioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The ratioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization ratioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.