Influence of multi-look processing to SAR ship detection

Speckle characteristics was analyzed by computing the ENL (Equivalent Number of Looks) for sub images of 30 by 30 pixels from four RADARSAT-2 dual polarization products, two single look complex (SLC) products acquired in wide swath single beam mode with dual polarization of HH+HV and two ScanSAR Wide (SCW) products acquired in ScanSAR Wide beam mode with dual polarization of HH+HV, which lie in the sea area around Iberian Peninsula. The speckle analysis indicates that ENL value combined with the intensity value of the SAR image should be a feasible short cut to find ship signatures out of SAR image. Based on the ENL analysis of speckle characteristics, a data set for ship signature has been derived, from which the ship signature was measured by polarimetric synthesis since the polarimetric SAR signature is an effective way of utilizing the amplitude and phase information to characterize the polarization properties of microwave backscatter. Driven by the need for a realistic microwave backscatter of ship signature model, the detailed behavior of a single ship in different polarizations and different incidence angles will be investigated by the Ray-Optical method which was developed by Burkholder et al. The purpose of this paper is to pave the way for a further improved scatter matrix model.

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

An explicit form of the linear multichannel synthetic aperture radar (SAR) intensity filter, which preserves radiometry while optimally reducing speckle is derived, together with a compact expression for the theoretical gain in equivalent numbers of looks (ENLs). The filter can be applied to mixed data types, which is demonstrated using a combination of ERS and JERS satellite data, and confirms the filter performance predicted by the theory. Tests indicate that a simplified form of the filter, which neglects correlation between images, gives an ENL only slightly less than optimal, while being much easier to implement. Exact analysis of the effect of estimating filter weights shows that the linear increase in ENL with the number of images predicted for the ideal filter does not occur. In practice, the ENL is affected by the window size used to estimate the weights and saturates as the number of images increases. An efficient recursive form of the filter is described, which is most naturally applied to multitemporal data for the practically important case where the current image is uncorrelated with previous images in a data sequence.

The equivalent number of looks (ENL) is an important parameter in the multilook statistical model of polarimetric synthetic aperture radar (Pol-SAR). Recently, the maximum likelihood (ML) method was proposed and gave a good performance in the Gaussian model case by using the full covariance matrix instead of the intensity of Pol-SAR data, but it generated underestimates in the product model case. In this paper several novel ENL estimators are presented via certain cumulants of the log-determinant of the sub-matrices of the multilook polarimetric covariance matrix. The texture effect to the ENL estimates is eliminated, and the analytic estimators are derived. The estimators use the full covariance matrix and sub-matrices information, rather than the intensities of polarization channels. All the novel estimators are suitable for any texture model and thus provide more accurate results than many existing ones. Experiments using simulated data and real data are presented to evaluate the performance of different estimators. The results show that the second log-determinant moment (SLDM3)-based method is the best one among the novel estimators. At the same time this estimator has much less computational complexity. In addition, a novel distribution classification method is proposed by coloring the image via second- and third-order log-cumulants of the covariance matrix (MLC), which is helpful to assess the estimation result.

An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images

To evaluate despeckling performance of SAR images using the equivalent number of looks(ENL) and edge keeping index(EKI), a new unsupervised evaluation method is proposed. First, ratio-based edge strength map(RESM) and direction information are calculated by anisotropic Gaussian kernel(AGK) bi-windows. Second, SAR image is divided into homogeneous regions and edge regions by thresholding RESM with a threshold estimated by an adaptive method, and thin edges can be extracted by the non-minimun suppression. Third, each local ENL of all pixels in homogeneous regions is estimated, and each local EKI of all pixels in edge regions is estimated. Then contrast curves can be obtained by statistical method for all local ENL and EKI. Experimental results show that the proposed method need not select estimation regions by artificial operation, and can get over insufficient evaluation and avoid the deviation estimation produced by the selection of different estimation regions.

Speckle is modeled as a signal dependent noise, which tends to reduce the image resolution and contrast, thereby reducing the diagnostic values of the ultrasound imaging modality. Reduction of speckle noise is one of the most important processes to increase the quality of biomedical images. Filters are used to improve the quality of ultrasound images by removing the noise. This paper compares the performance of the thresholding technique Bayes Shrink in despeckling the medical ultrasound images with other classical speckle reduction filters like Lee, Frost, Median, Kaun, Wavelet Bayes, Anisotropic diffusion and Wavelet. The performance of these filters is analyzed by the statistical measures such as Peak Signal-to Noise Ratio, Mean Square Error and Equivalent Number of Looks. To produce a better quality resolution picture, the filter should have high Peak Signal to Noise Ratio, low Mean Square Error, high Equivalent Number of Looks. The results obtained are presented in the form of filtered images, statistical tables and graphs. Finally, the best filter has been recommended based on the statistical and experimental results. From the results obtained Lee and Frost filter outperforms the other mentioned filters in terms of high PSNR and low MSE for high variance of noise where as anisotropic diffusion filter outperforms with high PSNR and low MSE with maximum ENL for low variance values of noise.

With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.

In this paper, speckle removal from synthetic aperture radar (SAR) images using subspace-based technique is proposed. The fundamental principle is to decompose the vector space of the noisy image into signal-plus-noise subspace and the noise subspace. Noise reduction is achieved by removing the noise subspace and estimating the clean image from the remaining image subspace. Linear estimation of the clean image is performed by minimizing image distortion while maintaining the residual noise energy below some given threshold. Since the noise is considered to be additive with subspace technique, a homomorphic framework is used to convert the multiplicative speckle noise into additive. The performance of the proposed approach is tested with simulated images and with real SAR images, and compared with Lee filter. The results indicated significant improvements by the proposed technique in terms of structural similarity index measure (SSIM) and equivalent number of looks (ENL).

Synthetic Aperture Radar (SAR) imagery is available all the time and, in all weathers, due to its ability to penetrate through cloud cover, dust, haze except heavy rainfall. SAR imagery mainly characterizes structural properties of various earth objects and provides a data which is rich in spatial information. Processing of SAR data has been of great interest due to these advantages. However, due to coherent processing of SAR signal, imagery is corrupted by speckle noise which is multiplicative in nature. Reduction of speckle noise from SAR imagery is helpful for analysis and interpretation of SAR images. In this paper, performance of conventional speckle reduction filters, namely Lee, Enhanced Lee, Frost, Enhanced Frost and Gamma MAP are compared with wavelet-based filtering technique for RISAT-1 imagery. Results are compared visually and using quality metrics, Speckle Suppression Index (SSI), Equivalent Number of Looks (ENL) and Speckle Suppression and Mean Preservation Index (SMPI). From visual interpretation of de-speckled image, it is evident that wavelet-based filtering out performs the conventional filters in terms of edge and texture preservation.

The channel imbalance and cross-talks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) SAR systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, etc. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and cross-talks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross-polarization and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the cross-talks. Then the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

Non-local means (NLM) filter is one of the state-of-the-art denoising filters. It exploits the presence of similar features in an image and averages those similar features to remove noise. However, a conventional NLM filter shows somewhat inferior performance of noise reduction around edges, suffering from low efficiency of collecting similar features to be averaged. In order to overcome this phenomenon, we propose a NLM filter with double Gaussian anisotropic kernels as a substitute for the conventional homogeneous kernel to effectively remove noise from OCT images corrupted by speckle noise. The proposed filter was evaluated by comparing with various denoising filters such as conventional NLM filter, median filter, bilateral filter, and Wiener filter. The fingertip OCT images, which were processed with the different denoising filters, indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI). A human retina OCT image was also used to compare and show the performances of noise reduction among different filters. In addition, the denoising performance with the proposed NLM filter was also investigated in the synthetic images for fair comparison among the filters by calculating the peak signal-to-noise ratio (PSNR). The proposed NLM filter outperformed the conventional NLM filter as well as the other filters.

In this paper, we propose a synthetic aperture radar (SAR) image despeckling method based on patch ordering and transform-domain filtering. Logarithmic transformation with bias correction is applied to the original SAR image to transform the multiplicative noise model into the additive model. Then, we adopt a two-stage filtering strategy. The first stage is coarse filtering which can suppress speckle effectively. In this stage, we extract the sliding patches from the logarithmic SAR image, and order them in a smooth way by a simplified patch ordering algorithm specially for SAR images. The ordered patches are filtered by learned simultaneous sparse coding (SSC), a technology recently advanced in image processing. Then, the coarse filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. The second stage is refined filtering which can eliminate small artifacts generated by the coarse filtering. In this stage, the sliding patches are extracted from the coarse filtering result and ordered in the same way. Then, we apply 2-D wavelet hard-thresholding to the ordered patches and reconstruct the refined filtering result. The final result is obtained by taking exponential transformation to the refined filtering result. An algorithm based on the proposed strategy is presented in detail and the parameters are selected for fast and effective realization. Experimental results with both simulated images and real SAR images demonstrate that the proposed method achieves state-of-the-art despeckling performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, equivalent number of looks (ENLs), and ratio image.

Generalized Kittler and Illingworth minimum-error thresholding (GKIT) algorithm was proposed by G.Moser for change detection in synthetic aperture radar (SAR) images with non-Gussion distribution. In this paper, we present an improved GKIT approach for unsupervised change detection from synthetic aperture radar (SAR) amplitude images by relaxing the demand of the same equivalent number of looks (ENL) in the GKIT approach based on Nakagami model. Experimental results on actual SAR images are given to demonstrate the validity of our proposed method.

A quadrilateral filtering algorithm is proposed in this letter to reduce noise in video synthetic aperture radar (video SAR). The novel filtering approach draws inspiration from the traditional bilateral filter and effectively exploits the similarities in both greyscale and geometric levels. Moreover, the proposed quadrilateral filter incorporates the similarity of time domain and rank-ordered absolute difference (ROAD) statistic for detecting the abundant speckle and impulse noise in video SAR frames. An impulse detector, which is related to the distribution of ROAD, is added in the filtering process to remove the outliers in the intensity domain after time redundancy processing with the target frame image. The proposed quadrilateral filter can obviously smooth various sources of noise, including strong speckle noise and impulse noise, while considering the details of each image frame of the video SAR. The new algorithm has been verified to achieve superior filtering performance with equivalent number of looks (ENL) values increased by 25% on average comparing with other widely used algorithms.