SAR image denoising based on orthogonal bandlet domain with context modeling

Synthetic Aperture Radar (SAR) images are widely used for a variety of applications such as surveillance, agricultural assessment and classification, planetary and celestial investigations, geology and mining, etc., due to its remarkable characteristic of capturing it under all weather conditions. SAR images are highly prone to speckle noise due to the ingrained nature of radar backscatter. Speckle removal is highly essential to limit the difficulty encountered while processing the SAR images. An exhaustive work has been done by researchers to despeckle SAR images using spatial filters, wavelet transform, and hybrid approaches. This work aims at exploring the different despeckling techniques to identify the best and suitable methodology. On measuring the despeckling performance using Peak Signal-to-Noise Ratio, Edge Preservation Ratio, Speckle Suppression Index, Speckle Suppression and Mean Preservation Index, and Structural Similarity Index simultaneously for the various techniques experimented, ridgelet transform-based thresholding works well. It gives better results by applying ridgelet transform and processing the subbands with minimax thresholding. The type and characteristics of the scene imaged also influence the result.

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. In this paper, we proposed a speckle reduction technique based on sparse representation and dictionary learning. Firstly, an adaptive dictionary was learned by performing KSVD algorithm through a large amount of training patches extracted from the noisy SAR image. Considering the inaccurate recovery of point targets which is brought by the inadequate number of training samples, we employed a point target enhancing scheme to highlight the important point targets in the SAR image. Some experiments were conducted on real SAR images, and the results shows that our proposed algorithm can effectively reduce the speckle noise as well as preserve details. Some comparisons are made to prove its superiority to the available algorithms.

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

We address the application of model-supported exploitation techniques to synthetic aperture radar (SAR) imagery. The emphasis is on monitoring SAR imagery using wide area 2D and/or 3D site models along with contextual information. We consider here the following tasks useful in monitoring: (a) site model construction using segmentation and labeling techniques, (b) target detection, (c) target classification and indexing, and (d) SAR image-site model registration. The 2-D wide area site models used here for SAR image exploitation differ from typical site models developed for RADIUS applications, in that they do not model specific facilities, but constitute wide area site models of cultural features such as urban clutter areas, roads, clearings, fields, etc. These models may be derived directly from existing site models, possibly constructed from electro-optical (EO) observations. When such models are not available, a set of segmentation and labeling techniques described here can be used for the construction of 2D site models. The use of models can potentially yield critical information which can disambiguate target signatures in SAR images. We address registration of SAR and EO images to a common site model. Specific derivations are given for the case of registration within the RCDE platform. We suggest a constant false alarm rate (CFAR) detection scheme and a topographic primal sketch (TPS) based classification scheme for monitoring target occurrences in SAR images. The TPS of an observed target is matched against candidate targets TPSs synthesized for the preferred target orientation, inferred from context (e.g. road or parking lot targets). Experimental results on real and synthetic SAR images are provided.

The speckle noise complicates the human and automatic interpretation of synthetic aperture radar (SAR) images. Thus, the reduction of speckle is critical in various SAR image processing tasks. In this paper, we introduce a new spatially adaptive wavelet-based Bayesian method for despeckling the SAR images. The wavelet coefficients of the logarithmically transformed reflectance and speckle noise are modeled using the zero-location Cauchy and zero-mean Gaussian distributions, respectively. These prior distributions are then exploited to develop a Bayesian minimum mean absolute error estimator as well as a maximum a posteriori estimator. A new context-based technique with a reduced complexity is proposed for incorporating the spatial dependency of the wavelet coefficients with the Bayesian estimation processes. Experiments are carried out using typical noise-free images corrupted with simulated speckle noise as well as real SAR images, and the results show that the proposed method performs favorably in comparison to some of the existing methods in terms of the peak signal-to-noise ratio, speckle statistics and structural similarity index, and in its ability to suppress the speckle in the homogeneous regions

Synthetic aperture radar (SAR) images suffer from speckle noise due to their imaging mechanism, which deteriorates image interpretability and hinders subsequent tasks like target detection and recognition. Traditional denoising methods fall short of the demands for high-quality SAR image processing, and deep learning approaches trained on synthetic datasets exhibit poor generalization because noise-free real SAR images are unattainable. To solve this problem and improve the quality of SAR images, a speckle noise suppression method based on subaperture decomposition and non-local low-rank tensor approximation is proposed. Subaperture decomposition yields azimuth-frame subimages with high global structural similarity, which are modeled as low-rank and formed into a 3D tensor. The tensor is decomposed to derive a low-dimensional orthogonal basis and low-rank representation, followed by non-local denoising and iterative regularization in the low-rank subspace for data reconstruction. Experiments on simulated and real SAR images demonstrate that the proposed method outperforms state-of-the-art techniques in speckle suppression, significantly improving SAR image quality.

Synthetic Aperture Radar (SAR) images are inherently degraded due to the coherent nature of the scattering phenomena called speckle. The presence of speckle decreases the utility of the SAR images by reducing the ability to detect ground objects. It affects the quality of the image adversely and hampers the observation of vital and crucial information present in the image. In this paper, we present speckle noise suppression techniques using wavelet decomposition and ICA methods. The algorithms viz., Projection Onto Approximation Coefficients (POAC), Projection Onto Span Algorithm (POSA) and Independent Component Analysis (ICA) are implemented on real SAR images and their results are tabulated. A comparison is made with standard speckle filters such as Lee filter, Frost filters, etc. The performance of our methods is measured in terms of Peak Signal-to-Noise Ratio (PSNR) values.

ABSTRACTThe speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.

Synthetic aperture radar (SAR) images are corrupted by speckle noise, which degrades the quality and interpretation of the images. Speckle removal provides a better interpretability of SAR images if the technique performs the filtering without loss of spatial resolution and preserves fine details and edges. This work aims to redefine the neighborhood areas around the noisy pixel and in this area the local mean and variance are computed to estimate the Maximum a Posteriori (MAP) filter parameters. The proposed modified MAP algorithm improves the ability to filter the speckle noise without blurring edges and targets by applying the MAP estimator in the current adaptive window that is controlled by a measure of homogeneity in the area around the noisy pixel. This adaptive windowing was also incorporated to the classical Kuan et al. (1985) and Frost et al. (1982) filters in order to evaluate the performance of the proposed scheme. The effectiveness in reducing speckle by the modified MAP filter is evaluated in terms of qualitative and quantitative aspects such as line and edge preservation and the improvement of the signal to noise ratio. The tests were performed in real SAR images.

An Improved Anisotropic Gaussian-Scale Invariant Feature Transform (IAG-SIFT) algorithm is proposed to register the Synthetic Aperture Radar (SAR) images. The standard SIFT algorithm generates isotropic Gaussian scale space which blurs lots of details in the image. The Anisotropic Scale Space (ASS) can preserve more details than the isotropic one. However, in many SAR registration methods, the gradient calculation of the ASS is performed by using the simple differential equation which is not appropriate for SAR images. As the SAR images contain multiplicative speckle noise, simple difference operation is not suitable for gradient calculation. It can reduce the number of correct matches in SAR image registration. So, in this paper, a Gaussian-Gamma-Shaped Bi-Window (GGS-BW) is used for ratio based gradient computation which is very effective for SAR images. The proposed IAG-SIFT method significantly improves the matching performance between the SAR images. The Experimental results show the effectiveness of proposed IAG-SIFT algorithm in SAR image registration.

The multiscale normalized cut technique is used to detect land/sea boundary in synthetic aperture radar (SAR) images. The proposed method is a region-based one, which is able to capture and utilize spatial information in the image. The real SAR images, e.g. ERS, ENVISAT and COSMO-SkyMed (CSK) SAR images, together with the Landsat-7 ETM+ optical data and in-situ GPS data were collected and used to validate the performance of the proposed method for coastline detection in SAR images.

Synthetic aperture radar (SAR) image segmentation is the key to SAR image automatic interpretation. However, speckle noise, intensity inhomogeneity, and irregular shaped objects with changing edge often make the SAR image segmentation very difficult, and existing algorithms have high computational complexity. We propose a region-based level set method using the local image intensity information. To represent the statistical characteristics of speckle noise, we first use a gamma statistical distribution to model every segmented SAR image. We then apply a modified region mean estimation formula to efficiently segment SAR images with inhomogeneity. Finally, Gaussian filtering is employed to regularize the level set function, which can avoid reinitialization. The experimental results on synthetic and real-world SAR images demonstrate that the proposed method has less computation cost, faster convergence rate, and more accurate segmentation results.