Synthetic Aperture Radar Image Enhancement Research Articles

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Nonlocal self-similarity is an important property of Synthetic Aperture Radar (SAR) images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images. Existing SAR ship detectors often independently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein. In this paper, we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance, basically including three stages: prescreening, intra-cue calculation, and inter-cue calculation. In the pre-screening stage, we design a new Histogram-based Density (HD) feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image. In the intra-cue calculation stage, target cues within a single candidate sub-image are extracted. In the inter-cue calculation stage, thanks to the nonlocal self-similarity among different candidate sub-images in terms of density features, we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images. Finally, for each candidate sub-image, we fuse its inter-cue and intra-cue to obtain final detection results. Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection performance than those of other state-of-the-art detection approaches.

Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation

In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

Hybrid spatio-frequency domain global thresholding filter (HSFGTF) model for SAR image enhancement

Abstract Synthetic Aperture Radar (SAR) deploys single antenna for both transmitting and receiving radar signals since it comes under mono static category. Major research work based on airborne and orbital focused on moving vehicle capturing, base penetration, greenery or vegetation penetration, methodologies to improve the quality of the captured images etc. In general, synthetic aperture radar imaging provides high level resolution images of vast regions under coverage. The pixel intensity of images in synthetic aperture radar is measured based on roughness, dimensional orientation and dielectric constant of plane captured. Processing of SAR images is mapping of signal data received by the radar in to a spatial image. Synthetic Aperture Radar images are easily vulnerable to speckle noises. These kinds of noises disrupt the image elucidation. This research article proposed the hybrid spatio-frequency domain global thresholding filter based pre-processing for SAR image enhancement for removal of additive as well as speckle noise which is more characteristic of any SAR images. The proposed image enhancement algorithm has been experimented under different noise variances and the results obtained are compared with other traditional methodologies.

Enhancement of SAR images using fuzzy shrinkage technique in curvelet domain

The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image.

Enhancement and Edge-Preserving Denoising: An OpenCL-Based Approach for Remote Sensing Imagery

Image enhancement and edge-preserving denoising are relevant steps before classification or other postprocessing techniques for remote sensing images. However, multisensor array systems are able to simultaneously capture several low-resolution images from the same area on different wavelengths, forming a high spatial/spectral resolution image and raising a series of new challenges. In this paper, an open computing language based parallel implementation approach is presented for near real-time enhancement based on Bayesian maximum entropy (BME), as well as an edge-preserving denoising algorithm for remote sensing imagery, which uses the local linear Stein's unbiased risk estimate (LLSURE). BME was selected for its results on synthetic aperture radar image enhancement, whereas LLSURE has shown better noise removal properties than other commonly used methods. Within this context, image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented using CPUs and commodity graphics processing units (GPUs). Experimental results demonstrate the reduction of computational load of real-world image processing for near real-time GPU adapted implementation.

Adaptive enhancement of compressed SAR images

Synthetic aperture radar (SAR) imagery is an important global all-weather surveillance and mapping satellite imagery system. As space-borne systems have a limited storage capacity, it is imperative to heavily compress SAR images, possible with lossy compression schemes. As a result, SAR images need to be enhanced in earth stations. The work reported in this paper aims to address the issue of compression artefact removal of SAR images in an adaptive manner. The SAR images, compressed using the JPEG utility at significantly low bit rates, are enhanced by adaptively removing coding artefacts and speckle noise. As edges carry significant information in satellite imagery, a significant edge image is used for edge enhancement with selective removal of noisy edges. Further, an image sharpness metric is proposed in this work to serve as an objective no-reference metric for measuring the sharpness of SAR images.

Enhancement of SAR images using curvelet with controlled shrinking technique

ABSTRACTThe speckle noise is the primary constraint in synthetic aperture radar (SAR) images. It will affect the features in the image and cause misinterpretation of objects. In this letter, curvelet with controlled shrinking technique is proposed to suppress speckle noise in SAR images. In this method, the curvelet transform is applied to SAR image. It decomposes image into different levels based on frequency. Application of using threshold and controlled shrinking function in the curvelet transform suppresses the noise coefficients. The results are favourable compared with some traditional techniques in terms of performance as well as preserving the details of the image.

Enhanced ISAR imaging method using back‐projection and SVA algorithm

ABSTRACTWe present an inverse synthetic aperture radar image enhancement technique that combines the plane‐wave approximation‐based back‐projection (BP) algorithm and the spatially variant apodization (SVA) method. The SVA technique is applied to reduce the sidelobe levels while retaining the cross‐range resolution. To demonstrate the performance of our algorithm, we compare the conventional fast Fourier transform (FFT)‐based algorithm, the combined FFT and SVA method, the BP algorithm, and the proposed method (combined BP and SVA method). The cross‐range resolution and peak to sidelobe ratio (PSLR) are selected as performance measures. Our algorithm has better resolution than the conventional combined FFT and SVA method, while the combined FFT and SVA method has better PSLR than our method. We validate our algorithm via a numerical simulation and measurements conducted in a compact range. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:993–997, 2015

Brief Communication: Contrast-stretching- and histogram-smoothness-based synthetic aperture radar image enhancement for flood map generation

Abstract. Synthetic-aperture-radar-image-based flood map generation is usually a challenging task (due to degraded contrast). A three-step approach (based on adaptive histogram clipping, histogram remapping and smoothing) is proposed for generation of a more visualized flood map image. The pre- and post-flood images are adaptively histogram equalized. The hidden details in difference image are enhanced using contrast-based enhancement and histogram smoothing. A fast-ready flood map is then generated using equalized pre-, post- and difference images. Results (evaluated using different data sets) show significance of the proposed technique.

Application of a Fully Automatic Autofocusing Algorithm for Post-Processing of Synthetic Aperture Radar Images based on Image Entropy Minimization

In this paper an application of an autofocusing algorithm, previously developed by the authors for the case of Inverse Synthetic Aperture Radar (ISAR) [i.e. airborne radar targets], is presented here for the case of Synthetic Aperture Radar (SAR) geometry [i.e. ground radar targets]. Here, a new theory and methodology for generating SAR synthetic backscattered data is also developed. This algorithm is named ‘CPI-split-algorithm’, where CPI stands for ‘Coherent Processing Interval’. Moreover, two simulation scenarios are presented for a ship target, which is located on the sea surface. In the first simulation scenario, the ship is considered at first to be stationary, and subsequently an oscillatory movement is induced to its position along the vertical axis, due to sea surface motion. In the second simulation scenario, a partial loss of data is examined, caused by temporary accidental malfunctioning of radar transmitter or receiver, assuming that the target (ship) is stationary. Numerical results presented in this paper show the effectiveness of the proposed autofocusing algorithm for SAR image enhancement. DOI: http://dx.doi.org/10.5755/j01.eee.19.6.4573

Markov Random Field Models for Non-Quadratic Regularization of Complex SAR Images

This paper presents a comparison between Markovian models for Synthetic Aperture Radar (SAR) image despeckling within the complex domain. The novelty of this paper is enhancement of single look complex SAR images and information extraction. The Gauss-Markov Random Field model, Auto-binomial and Huber-Markov Models are used with the non-quadratic regularization. The experimental results using synthetic generated images and real SAR images showed that the best results were obtained with the Auto-binomial model followed by the Gauss-Markov Random field, and finally the Huber-Markov model, for synthetic generated data and real single look complex SAR images.

Sar Image Denoising Based on Multifractal and Regularity Analysis

Synthetic Aperture Radar (SAR) images are corrupted by multiplicative speckle noise, which is due to random interference of electromagnetic waves. The noise degrades the quality of images and makes them hard to be interpreted, analyzed and classified. It appears sensible to reduce speckle in SAR images, while the structural features and textural information are not lost. This paper applies the framework of multifractal and regularity analysis to SAR image enhancement and denoising. The method does not make explicit assumptions about the model of the noise, but rather supposes that image denoising is equivalent to increasing the Hölder exponent at each point. The image is characterized via its multifractal spectrum, which mode yields the most frequent Hölder exponent. This manipulation leads to a smooth image while preserving the useful information in the signal. In order to evaluate the restoration result, Equivalent Number of Look (ENL) and edge save index (ESI) are used as criterion. Better result is obtained when regularity increase equal 0.5 (δ=0.5).

Sar Image Denoising Based on Multifractal and Regularity Analysis

Synthetic Aperture Radar (SAR) images are corrupted by multiplicative speckle noise, which is due to random interference of electromagnetic waves. The noise degrades the quality of images and makes them hard to be interpreted, analyzed and classified. It appears sensible to reduce speckle in SAR images, while the structural features and textural information are not lost. This paper applies the framework of multifractal and regularity analysis to SAR image enhancement and denoising. The method does not make explicit assumptions about the model of the noise, but rather supposes that image denoising is equivalent to increasing the Hölder exponent at each point. The image is characterized via its multifractal spectrum, which mode yields the most frequent Hölder exponent. This manipulation leads to a smooth image while preserving the useful information in the signal. In order to evaluate the restoration result, Equivalent Number of Look (ENL) and edge save index (ESI) are used as criterion. Better result is obtained when regularity increase equal 0.5 (δ=0.5).

Applications of ICA for the enhancement and classification of polarimetric SAR images

Independent components analysis (ICA) based methods for polarimetric synthetic aperture radar (SAR) image speckle reduction and ground object classification are studied. Several independent components can be extracted from polarimetric SAR images using ICA directly. The component with lowest speckle index is regarded as the scene after speckle reduction. The disadvantage of this method is that only one image is kept and most polarization information will be lost. In this paper, we use ICA‐sparse‐coding shrinkage (ICA‐SPS) based speckle reduction method, which is implemented on each individual image and can keep polarization information. It is carried out on the combined channels obtained by Pauli‐decomposition rather than original polarization channels in order to keep relative phase information among polarization channels and get better performance. After ICA‐SPS, the effect of speckle suppression on SAR image classification can be compared favourably with other methods by combining the channels into a false colour image. At last, a new ICA‐based classification method is presented. In this method, four independent components are separated by ICA from five polarization and combined channels. One of these independent components which includes little ground object information is regarded as speckle noise and therefore be discarded. The remaining three components can be treated as subordination coefficients of three kinds of targets. A classified image can be obtained based on the components. And by composing these three channels in RGB colour pattern, a false colour image can be constructed.

Generalized Gaussian Decompositions for SAR Enhancement Using Analysis-by-Synthesis

Britton, D. F., and Smith, M. J. T., Generalized Gaussian Decompositions for SAR Enhancement Using Analysis-by-Synthesis, Digital Signal Processing 12 (2002) 284–294 Quality enhancement algorithms for synthetic aperture radar (SAR) images offer the potential to increase the accuracy of analysis and threat assessment. This paper presents a new SAR image enhancement method for that purpose. The method is built around a Gaussian representation of the image using analysis-by-synthesis as the engine for the approximation. Its sequential structure and synthesis property enables object boundaries and speckle characteristics to be decoupled and modified to enhance the quality. Evaluations demonstrate that the algorithm helps improve the performance of automatic target recognition systems as well as enhance subjective appearance.

SAR image enhancement via post-correlation signal processing

Seventeen interpreters ranked sets of computer-generated radar imagery to assess the value of post-correlation processing on the interpretability of SAR (synthetic aperture radar) imagery. The post-correlation processing evaluated amounts to a nonlinear mapping of the signal exiting a digital correlator and allows full use of signal bandwidth for improving the spatial resolution or for noise reduction. The results indicate that it is reasonable to hypothesize an optimal SAR presentation format for specific appliations even though this study was too limited to be specific.