SAR Image Enhancement based on Wavelet Filter and Gaussian Mixture Model

The SEASAT satellite system, using an onboard 23-cm-wavelength synthetic aperture radar (SAR), collected approximately 25- to 40-m-resolution radar images of the ocean in 100-km-wide swaths ranging in length from about 300 to 3000 km. Here we report results from the 18 SEASAT SAR passes during the Joint Air-Sea Interaction (JASIN) experiment conducted off the west coast of Scotland in the summer of 1978. These many SAR images, when coupled with the intensive ship, buoy, and aircraft measurements of the JASIN experiment, provide a unique opportunity to assess the ability of satellite SAR to measure ocean surface phenomena, particularly surface wave fields. In this study we use only optically processed SAR images. Although gravity waves of length approximately 80 to 300 m are often seen in SAR imagery, they are not always seen. We find that SAR resolution and wave height are important criteria for determining wave visibility and suggest that wind velocity is also. Comparisons between SAR and buoy estimates of dominant wavelength and direction (including data from several other experiments) agree to within about ±14% and ± 10°, respectively. We use a focus sharpness algorithm to resolve the 180° directional ambiguity of SAR image directional estimates. Correlation of buoy measurements of significant wave height (H 1/3) with peak signal- to-noise ratio in Fourier transforms of SAR images (r = 0.7) suggests that H 1/3 can be estimated to an accuracy of about ± 1 m. However, a similar, but weaker, correlation exists between signal-to-noise and wave length, i.e., H 1/3 and wavelength are correlated in the JASIN data set. SAR image power spectra are in rough agreement with buoy measurements of omnidirectional ocean wave height spectra Ψ(K) and correspond less closely with ocean wave slope spectra Ψ(K) However, the dominant wavenumber (Kpeak.) in SAR spectra typically falls below the dominant wavenumber of corresponding buoy spectra. Further, the slope of SAR spectra for K K Peak is typically less steep than corresponding buoy spectra Ψ(K). These differences between SAR spectra and buoy measurements of form qualitative support for the imaging mechanisms developed by Alpers, Ross, and Rufenach. Analysis algorithms based on this theory, but including empirical modifications, should substantially improve estimates of Ψ (K,θ) using SAR images.

Very-high resolution (VHR) synthetic aperture radar (SAR) images from the last generation satellites such as TerraSAR-X and TanDEM-X exhibits special characteristics, especially in the urban-areas. Consequently, attention is needed on special considerations while developing algorithms for SAR image processing and its applications for automated interpretation. With automatic interpretation we refer to the information extraction and characterization for image categorization, retrieval, segmentation, automated target recognition etc.. In this article we focus our attention to the problem of SAR image categorization. The interest in SAR image categorization in VHR SAR (on the contrary to the pixel-based classification in low-resolution SAR images) has increased with enhanced resolution providing opportunity to carry out a more detailed analysis of targets and objects. SAR image categorization requires generation of a compact feature descriptor which accurately define the image content. A feature descriptor can be generated using ‘parametric’ or ‘non-parametric’ approaches in ‘image’ or ‘image within a transformation space’. The objective of this article is to review some selected techniques for this purpose in form of a methodological classification. Qualitative assessment of selected algorithms is presented.
\n
\n

In high-resolution (HR) and very-high resolution (VHR) synthetic aperture radar (SAR) images, focus is now on the patch-oriented image categorization in contrast to the pixel-based classification in low-resolution SAR images. SAR image categorization requires the generation of a compact feature descriptor that accurately defines the content of the image patch under consideration. In this paper we propose a parametric feature descriptor generated on the complex-valued SAR image within a transformation space. The fractional Fourier transform (FrFT), has been considered to transform the image pixels of the single-look complex (SLC) SAR images in order to obtain a simpler statistical response. The real and imaginary components of the complex-valued FrFT coefficients have been modelled with generalized Gaussian distribution (GGD). The proposed feature descriptor is compared with a Wavelet-decomposition-based parametric feature descriptor; and with the FrFT-based and Gabor-filter-bank-based non-parametric feature descriptors. Categorization accuracy enhancement is demonstrated over several categories comprising of natural topologies. The experimental database consists of 2000 image patches (of size 200 × 200 pixels) extracted from SLC HR TerraSAR-X scenes.

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, 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.

More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

This paper proposes a novel method on Synthetic Aperture Radar (SAR) Image denoising, which is based on context modeling combined with multiscale orthogonal bandlet coefficients. For the main influence of SAR image is multiplicative speckle noise, the logarithm uniform transform is used to convert it to additive noise. In this paper, the multiscale orthogonal bandlet transform is applied to noisy SAR Image. The contextual model is used to compute contextual values of bandlet coefficients on each scale. Therefore, bandlet coefficients are reconstructed into some parts by contextual values. The spatially adaptive soft-threshold (T) is to denoise SAR image in each part. Experimental results demonstrate that this method outperforms the methods of Ehance-Lee (E-lee), contextual model based on wavelet domain (Wavelet-context), and Hidden Markov Tree model bases on wavelet domain (Wavelet-hmt) on there indexes: Floating index (FI), Standard deviation and Edge saving index of vertical direction, by using a real SAR image. However, the method can not only reduce the speckle noise but also preserve edges and high frequency information, and gets considerable visual effect on SAR Image denoising.

Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetration. Limited by its working principles, SAR images are not easily interpreted, and fusing SAR images with optical multispectral images is a good solution to improve the interpretability of SAR images. This paper presents a novel image fusion method based on non-subsampled shearlet transform and activity measure to fuse SAR images with multispectral images, whose aim is to improve the interpretation ability of SAR images easily obtained at any time, rather than producing a fused image containing more information, which is the pursuit of previous fusion methods. Three different sensors, together with different working frequencies, polarization modes and spatial resolution SAR datasets, are used to evaluate the proposed method. Both visual evaluation and statistical analysis are performed, the results show that satisfactory fusion results are achieved through the proposed method and the interpretation ability of SAR images is effectively improved compared with the previous methods.

A novel three-step method to reduce speckle in Synthetic Aperture Radar (SAR) images is presented. First, an edge detector is used to detect the edge orientations in SAR images. Second, using a data processing method named empirical mode decomposition, the SAR image is smoothed along four directions: horizontal, vertical, left diagonal and right diagonal, respectively. Third, the SAR image is reconstructed such that if the edge direction is horizontal, the pixel smoothed along the horizontal direction is used to reconstruct the image, and so on; if an edge is not detected, the mean of the four directionally smoothed images is used to reconstruct the image. Application of this method to SAR images has shown that it is well balanced in the quality of visual appearance, mean preservation, edge preservation, and reduction of the standard deviation.

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.

Convolutional Neural Network (CNN) has shown great potential for structural information extraction, however, Synthetic Aperture Radar (SAR) image interpretation via CNN, is still a challenging task due to the pixel- to- pixel variation nature of SAR signal. In this paper, a pattern strengthened deep model for SAR image classification is presented. The main idea underlying this method is to strengthen (or sharpen) the salient patterns contained in SAR image while attenuate the random variation, so as to leverage the enormous potential of deep model. To achieve this goal, max pooling is elaborately designed to form a pattern strengthened deep model. The experimental results on two SAR datasets show that the proposed method increases the classification accuracy by 4.91% and 4.64% compared with the typical CNN, and indicate our innovative attempt is promising for SAR image classification in the case of limited training data.

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.

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

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.