Real SAR Data Research Articles

A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR

Permanent Scatterer SAR Interferometry (PSInSAR) aims to identify coherent radar targets exhibiting high phase stability over the entire observation time period. These targets often correspond to point-wise, man-made objects widely available over a city, but less present in non-urban areas. To overcome the limits of PSInSAR, analysis of interferometric data-stacks should aim at extracting geophysical parameters not only from point-wise deterministic objects (i.e., PS), but also from distributed scatterers (DS). Rather than developing hybrid processing chains where two or more algorithms are applied to the same data-stack, and results are then combined, in this paper we introduce a new approach, SqueeSAR, to jointly process PS and DS, taking into account their different statistical behavior. As it will be shown, PS and DS can be jointly processed without the need for significant changes to the traditional PSInSAR processing chain and without the need to unwrap hundreds of interferograms, provided that the coherence matrix associated with each DS is properly “squeezed” to provide a vector of optimum (wrapped) phase values. Results on real SAR data, acquired over an Alpine area, challenging for any InSAR analysis, confirm the effectiveness of this new approach.

An Interpolated Phase Adjustment by Contrast Enhancement Algorithm for SAR

Phase adjustment by contrast enhancement (PACE) is an autofocus algorithm that is capable of performance that is unattainable by conventional techniques. It is a nonparametric method that requires no constraints on the type of phase error to be measured. The algorithm does not require special data culling techniques or the presence of isolated scatterers. However, the drawback of PACE algorithm is that the number of estimated variables is very large; it leads to a long computational time. The azimuth sampling frequency is commonly much bigger than the bandwidth of phase error in SAR image so that we can estimate part of the phase error variables and then obtain the whole variables by interpolation; this induces the interpolated phase adjustment by contrast enhancement (IPACE) algorithm. The IPACE algorithm can remarkably reduce the computational time while maintaining the accuracy. This letter has derived the detailed processing of IPACE, and the results of the experiments using real SAR data are presented to show the validity of the proposed algorithm.

A new research on sea surface wind direction retrieval of synthetic aperture radar image

A new method to retrieve sea surface wind direction using synthetic aperture radar image is presented. Some pretreatment steps such as suppressing noise, down sampling and dividing into several sub-images are presented first. The method of two-dimensional numerical differentiation based on Tikhonov regularization is introduced to compute the gradient direction in the sub-image intensity of every point. The distance weighted objective function is used to compute the overall gradient direction in the sub-images. The retrieved wind direction is orthogonal to the overall gradient. When adding 10% of random errors, numerical simulations show that the result of numerical differentiation is better than that from the Sobel operator which is based on the finite difference method: the former error is 0.7293 , and the latter error is 13.5069 . Finally, an experiment based on real SAR data shows that the wind direction deviates from the overall direction determined by the method of Sobel operator, and the direction determined by the method of numerical differentiation is consistent well with the overall wind direction. Comparing the ship board wind direction and the corresponding location wind direction retrieved from the synthetic aperture radar sub-images with the two methods, the average result error of Sobel operator method is 9.0331, while that of numerical differentiation method is 1.1148. This new method is an effective and high accurate method to retrieve the sea surface wind direction of synthetic aperture radar image.

Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data

This paper concerns forest parameter retrieval from polarimetric interferometric synthetic aperture radar (PolInSAR) data considering two layers, one for the ground under the vegetation and one for the volumetric canopy. A model is designed to combine a physical model-based polarimetric decomposition with the random-volume-over-ground (RVoG) PolInSAR parameter inversion approach. The combination of a polarimetric scattering media model with a PolInSAR RVoG vertical structure model provides the possibility to separate the ground and the volume coherency matrices based on polarimetric signatures and interferometric coherence diversity. The proposed polarimetric decomposition characterizes volumetric media by the degree of polarization orientation randomness and by the particle scattering anisotropy. Using the full model enhances the estimation of the vertical forest structure parameters by enabling us to estimate the ground-to-volume ratio, the temporal decorrelation, and the differential extinction. For forest vegetation observed at L-band, this model accounts for the ground topography, forest and canopy layer heights, wave attenuation in the canopy, tree morphology in the form of the angular distribution and the effective shapes of the branches, and the contributions from the ground level consisting of surface scattering and double-bounce ground-trunk interactions, as well as volumetric understory scattering. The parameter estimation performance is evaluated on real airborne L-band SAR data of the Traunstein test site, acquired by the German Aerospace Center (DLR)'s E-SAR sensor in 2003, in both single- and multibaseline configurations. The retrieved forest height is compared with the ground-truth measurements, revealing, for the given test site, an average root-mean-square error (rmse) of about 5 m in the repeat-pass configuration. This implies an improvement in rmse by over 2 m in comparison to the pure coherence-based RVoG PolInSAR parameter inversion.

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.

Building extraction based on stereo analysis of high-resolution SAR images taken from orthogonal aspect directions

In this paper high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their heights from the different layover effects in perpendicular taken views. Due to the strong dependence of the appearance of objects on the lighting and viewing direction, it is unlikely that a simple image-matching method would succeed. Instead, higher level object matching is proposed. Here, a knowledge-based approach is applied, a production system. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows, rectangular structures, or symmetries. The stereo analysis is then accomplished locally by means of productions that combine and match these image objects and infer the height. The approach is tested using real SAR data of an urban scene.

Results of flight tests of a two-channel radar system with real-time MTI processing

Space–time adaptive processing (STAP) in real-time is discussed for two applications: first, STAP can be used for suppressing clutter and jammers; moreover, clutter filter coefficients gained by STAP can be used for the purpose of the correct positioning of moving targets within SAR images. Results of this real-time STAP technique as well as the results of the moving-target repositioning technique are presented: the clutter-suppression quality and the moving target repositioning accuracy are demonstrated with real SAR data and moving targets recorded during a measurement campaign.

Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee and Frost filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

ラジスキー距離を導入したポラリメトリックＳＡＲ画像データの土地被覆分類における特徴ベクトルの拡張

In this paper, expansion of feature vector using Rajski distance image is proposed to improve the accuracy of landcover classification using polarimetric SAR image data. To indicate a reason for the introduction of Rajski distance, simulation of Rajski distance value for the combination of fundamental scattering models is executed, and the results are inspected by comparison with calcurated value from some real polarimetric SAR data. To compare the classification accuracy for the different feature vector ; one of them is constructed only by amplitude images and the other is added Rajski distance images, the availability is shown by Euclid distance method and maximum likelihood method as supervised classifier. The expansion of feature vector proposed in this paper is able to be realized using amplitude images only since this method does not require any phase information.

Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization

There is an ever-growing interest in the compression of SAR data because of the huge resources required for storage and transmission. This is especially true for spaceborne sensors, given the limited capacity of the downlink channel. Unfortunately, SAR data lack the useful properties on which compression algorithms rely; indeed, these are present in the focused images, but focusing is too complex for on-board implementation at this time. Poggi et al. (2000) proposed to perform on the satellite only the low-complexity range focusing, which increases the data correlation and better concentrates their energy. These properties were then exploited by adopting a variable-rate vector quantizer, with a clear performance improvement with respect to reference techniques. However, vector quantization (VQ) is too complex for actual on-board implementation, and therefore, here we replace VQ with trellis-coded VQ. To limit complexity, only small vectors are used, which reduces VQ's ability to exploit data dependencies; on the other hand, trellis coding allows one to encode large blocks of data at once, and to obtain a better partition of the input space. Experiments on real SAR data show that the overall performance is comparable to that of Poggi et al., but the complexity is much lower, making on-board implementation possible.

A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing

This paper presents a new two-dimensional (2-D) phase unwrapping (PhU) algorithm based on a least squares (LS) region growing strategy: the wrapped phase image is partitioned in different regions that are sequentially unwrapped via a LS algorithm. Reliable regions are dealt with at the beginning of the procedure, while critical areas are unwrapped in the final steps, thus avoiding error propagation from critical to reliable areas. A conditioned least squares formulation of the phase unwrapping problem is the core of the proposed procedure: this allows the solution to be tied to some known boundary phase values, thus guaranteeing the correct joining of the reconstructed phase in between the different regions and preventing them from being independently unwrapped. The application of the finite element method allows a straightforward implementation of the algorithm in the discrete domain case. Experimental results, carried out on simulated and real interferometric SAR data, show the effectiveness of the proposed algorithm and the improved performances with respect to existing unwrapping procedures.

A new MTI-SAR approach using the reflectivity displacement method

A new method for moving target detection, localization, and velocity estimation using SAR raw data is presented which allows the determination of the target's complete velocity vector. The method is applicable if the target has either a tangential velocity (/spl mu//sub T//spl ne/0) or a radial acceleration (/spl nu//spl dot//sub R//spl ne/0), or both. It is based on the analysis of the Doppler rate map which is obtained from the range compressed raw data, applying the reflectivity displacement method (RDM). The tangential velocity of each moving target is obtained from its own Doppler rate and the radial velocity from its own linear range migration. A velocity accuracy better than 3 m/s can be achieved for the airborne case. The method is validated experimentally by the use of real SAR data obtained by DLR's E-SAR. The paper gives a short description of the state of the art of moving target velocity measurements, introduces the RDM for stationary targets and its extension to the application for moving targets. It furthermore shows experimental results. >

Real time SAR-data processing: Parallelism and granularity illusions and possibilities

The problem of precision processing of SAR data in real time is here discussed, considering both a new performance evaluation model for structured algorithms and effective computational schemes of the SAR data compression process, in order to assess conditions of effective real time processing possibilities in this performance demanding application. Our analysis leads us to propose f it is suitable to work in very high granularity systems of bit-sequential type (as “connectionist” architectures). Interconnection and data allocation policies, as well as possible process decomposition are also discussed. Results of the present investigation are the basis of a joint research program we have started with the Italian Space Agency (ASI) at the CGS Laboratory (Spatial Geodesy Center) in Matera (Italy).

Estimation of the two‐dimensional ocean current shear field with a synthetic aperture radar

A double inverse method to convert SAR data to estimates of current shear was developed theoretically. Problems of applying the technique to real SAR data were examined. The first step of the inversion process involved elimination of noise in the ( ) wave spectra deduced from SAR images. The second step, estimation of current shear, required the existence of well defined swell and a favorable single of interaction between swell and ocean current. Application of the method in the Antarctic Circumpolar Current encountered sea conditions that both satisfied and violated these requirements. Where the wave‐current geometry was favorable, realistic surface current velocity fields were obtained, suggesting the presence of a substantial mesoscale eddy. Another attempt to apply the method in the Gulf Stream produced wave spectral estimates that showed no clear peak and hence produced no current estimates.