Advances in Information Extraction of Surface Parameters Using Tomographic SAR

Attributed scattering center is one of important features of Synthetic Aperture Radar (SAR) image. In this paper, a method for the matching of attributed scattering centers and its application to SAR target recognition is proposed. First, the attributed scattering centers of the test SAR image and template SAR images are extracted on the basis of the attributed scattering model. Second, the Hungarian algorithm is employed to match the two scattering center sets. Based on the one to one correspondence, we design a new similarity measure to evaluate the similarity between the two scattering center sets that will decide the target type of the test image. The similarity measure considers the effects of each individual scattering center, single matching pair, and missing alarms and false alarms; thus, it is more comprehensive. The experiment based on moving and stationary target acquisition and recognition database demonstrates the validity of the proposed method.

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 vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this letter, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. Keywords: sea ice, data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model (Published: 18 September 2015) Citation: Tellus A 2015, 67, 27218, http://dx.doi.org/10.3402/tellusa.v67.27218

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.

Oil spill has tremendous effect on marine environment. Therefore, it is significant for protecting marine environment to get oil spill information timely. Presently, the methods which most countries adopt to detect oil spill are mainly direct detection or remote sensing methods. Among these methods, Synthetic Aperture Radar (SAR), one technique of remote sensing methods, has been becoming a hot research field nowadays. This paper concludes main characters of SAR images in oil spill detection. It introduces, compares and analyses the oil spill detection steps and realized techniques. There are four steps in oil spill detection process, including filtering, target detection, feature extraction and classification. Filtering can be completed by several filtering techniques, such as Lee, advanced Lee and Frost. General speaking, there are four methods can realize target detection, which are single threshold, adaptive threshold, wavelet transform and max entropy method. About the third step, feature extraction, 12 features can be used to represent oil spill. Classification is a key and difficult step in oil spill detection, in which algorithms based on Bayes and neural network can be adopted. Finally, the paper points out the development direction of oil spill detection in SAR images.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

SAR image formation scheme implementation and endorsement sprouting from Level-0 data decoding

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.

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

For a synthetic aperture radar (SAR) system, the platform attitude drift will affect the image qualities. During SAR system design computer simulation can be used to determine the attitude stability which can satisfy the users' demands. In the computer simulation, the first step is to create the mathematic model. General models of platform attitude drift are usually created in the time domain, such as the sinusoid one, and the model presented is done in the frequency domain. For each attitude angle (roll, yaw, pitch), the amplitude and phase values are first generated in the frequency domain. Because the attitude parameters are sampled with PRF (pulse repetition frequency) in SAR system, the maximum frequency is set to PRF/2. The amplitude may be changed with the frequency (for example, inversely proportional to the square of frequency), while the phase may be uniformly distributed between 0 to 2/spl pi/. Then the inverse Fourier transformation converts this data into the time domain. Because the phase center of the SAR's antenna is usually offset from the platform center, the attitude drift will change the slant range between the antenna's phase center and the target on the ground. These changes will affect the Doppler history in azimuth direction for the SAR. After these changes are simulated, the SAR raw data of a point target can be generated. With the general SAR procession, the simulated image is obtained. Using the analysis software, the authors can get the effects on the image qualities of the attitude drift. At last, the simulation steps and an example are also presented.

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.

In single band and single polarized synthetic aperture radar (SAR) images, the information is limited to intensity and texture only and it is very difficult to interpret such SAR images without any a priori information. For unsupervised classification of SAR images, M-band wavelet decomposition is performed on the SAR image and sub-band selection on the basis of energy levels is applied to improve the classification results since sparse representation of sub-bands degrades the performance of classification. Then, textural features are obtained from selected sub-bands and integrated with intensity features. An adaptive neuro-fuzzy algorithm is used to improve computational efficiency by extracting significant features. K-means classification is performed on the extracted features and land features are labeled. This classification algorithm involves user defined parameters. To remove the user dependency and to obtain maximum achievable classification accuracy, an algorithm is developed in this paper for classification accuracy in terms of the parameters involved in the segmentation process. This is very helpful to develop the automated land-cover monitoring system with SAR, where optimized parameters are to be identified only once and these parameters can be applied to SAR imagery of the same scene obtained year after year. A single band, single polarized SAR image is classified into water, urban and vegetation areas using this method and overall classification accuracy is obtained in the range of 85.92%–93.70% by comparing with ground truth data.

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.