Ground-based Synthetic Aperture Radar Data Research Articles

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification.

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical.

An improved method of GB-SAR phase unwrapping for landslide monitoring

The ground-based Synthetic Aperture Radar (GB-SAR) technique can be applied to the safety monitoring and early warning of geo-hazards, especially for monitoring displacement of various types of landslide masses. One of the key techniques of processing GB-SAR monitoring data is phase unwrapping, which can be dramatically affected by atmospheric humidity, atmospheric pressure, sampling interval, etc. In high mountains and valleys where environmental change is drastic, GB-SAR monitoring data is vulnerable to incoherence both spatially and temporally. Therefore, an improved phase unwrapping method of GB-SAR data for landslide monitoring is proposed in this paper, which can realize three-dimensional phase unwrapping in time and space. The method adopts the idea of sparse data processing and realizes phase unwrapping of monitoring data in two steps. Firstly, taking full advantage of the high temporal resolution of GB-SAR monitoring data, the one-dimensional phase unwrapping method in the spatial domain is applied to the time domain, and the interference phases of PS points are unwrapped in the time domain. Then, the PS network is constructed based on the time-phase unwrapping results, the double-difference phases of the network baselines are obtained and the spatial consistency adjustment is applied to them, and the spatial phase unwrapping can be realized by the indirect least square adjustment method. This method successfully solves the problem of path dependence, island isolation and large computation of the common method of space-borne SAR phase unwrapping, which can process GB-SAR data with high accuracy and efficiency.

Geocoding of ground-based SAR data for infrastructure objects using the Maximum A Posteriori estimation and ray-tracing

Ground-Based Synthetic Aperture Radar (GB-SAR) enables to monitor a scene in a distance up to a few km and with an accuracy of around one mm. It is mainly used for landslide monitoring but also used to monitor man-made structures such as bridges and dams. Due to the particular distance-dependent imaging concept and related geometric distortion effects, the interpretation of the SAR images can be difficult, especially in the case of complex man-made structures. The geocoding of the SAR data enables to project the 2D SAR image on a 3D digital elevation model (DEM) or a 3D terrain model and highly improves the interpretation of the observed deformation. It also allows to fuse SAR-based observations with other data sources. The purpose of this paper is to present a new algorithm to geocode GB-SAR data in complex scenarios. The method introduces two new components, a Bayesian statistical approach and a ray-tracing algorithm. The first one uses an a priori function on the received intensity to improve the geocoding in case of strong layover, while the latter considers areas in the radar shadow to be excluded from geocoding. Moreover, ray-tracing provides the starting point for the analysis of signals with multiple reflections which are also discussed in this paper. Our algorithm is applied to and evaluated with two case studies. The first one demonstrates the improved geocoding of GB-SAR data on the Linach dam in the Black Forest in Germany, which is characterized by a rather complex architecture. The second case study shows the application of the method at the Enguri dam in Georgia, which is characterized by its large dimensions and a dam height over 270m.

Clutter removal in millimeter wave GB-SAR images using OTSU’s thresholding method

In this study, a filtering method based on the threshold value of normalized synthetic aperture radar (SAR) data is proposed to eliminate clutter in millimeter wave ground based synthetic aperture radar (GB-SAR) images. In the proposed method, first, stepped frequency continuous wave SAR data are reconstructed by using the back-projection algorithm and focused complex SAR data are obtained. Then, the amplitude values of the complex SAR data are normalized and the best threshold values to distinguish the target from clutter is determined by the OTSU’s thresholding method. Next, a filter mask is created that cancels all data below the computed threshold values. The mask matrix is finally multiplied with the resulted GB-SAR data to eliminate all clutter from the image. With the proposed technique, the best threshold value is determined automatically by directly processing the raw data without converting the SAR data into any RGB images. The proposed technique is validated through real GB-SAR experiments that were carried out in the frequency band of 78-81 GHz. In the experiments, challenging GB-SAR data are obtained using high cluttered background materials, and very successful filtering operations are performed with the proposed technique.

Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

Dam Structure Deformation Monitoring by GB-InSAR Approach

Ground-based synthetic aperture radar (GB-SAR) has been proved to be one of the cutting-edge techniques for the timely detection of slope failures in both natural and engineered slopes. This paper focuses on the structure deformation monitoring on the dams using GB-SAR data. Temporal sequence data was collected by ground SAR equipment from 29 July to 1 August for the Geheyan dam and the SAR images with high quality were selected through the exhaustive spatial-temporal coherence analysis based on permanent scatterer (PS) theory in this paper. A practical solution for dam structure deformation extraction after the atmospheric effect reduction is proposed in depth. The deformation of the dam spillway gates is greater than that of the dam body monitored by this GB-SAR campaign, and with the increase of the water level in the reservoir area, the displacement increases along the direction of water flow gradually. The surface deformation rate of the dam body is fitted by linear regression analysis, and the interpolated rate results are compared and verified with the plumb line measurements. Finally, the consistency of the dam deformation average rate based on the PS time series analysis technology by GB-SAR and plumb lines is verified in this article, demonstrated the excellent performance of the proposed method for remote multipoint displacement measurements of the dam.

Three-Dimensional Ground-Based SAR Imaging Algorithm Based on Keystone Formatting and Subblock

Three-dimensional (3D) ground-based synthetic aperture radar (GB-SAR) draws attention because of the ability to obtain high accuracy 3D information of the monitoring terrain. Besides, the 3D GB-SAR system is welcome due to the flexibility and large coverage in angle and range. However, existing 3D imaging algorithms for GB-SAR data focusing have limits of high computational complexity or narrow applicable scope. To realize 3D displacement monitoring with high spatial resolution and short revisit time, in this paper, a novel 3D imaging algorithm is proposed. Based on characteristics of the model of echo data from the large range and wide-view angle scenario, the proposed method uses keystone formatting to complete range migration correction and subblocks dechirping to realize horizontal focus. With the method, the reflectivity of the monitoring scenario is obtained by only one time of linear interpolation and several times of fast Fourier transforms. The main advantages of this algorithm are high imaging precision and low computational cost, and in addition, it is applicable for large illuminating coverage including the near-field and the far-field of the radar aperture. The imaging results are sampled on pseudo-spherical grid, aiming to simplify the formulation. Finally, this method is extensively validated with simulations.

High-quality pixel selection in ground-based SAR interferometry based on pseudo- and quasi-permanent scatterers analyses

Due to the presence of phase noise and temporal decorrelation, high-quality pixel selection has been an indispensable part in ground-based synthetic aperture radar (GB-SAR) interferometry. The main pixel selection methods are permanent scatterer (PS) methods and coherent scatterer methods. However, both of them cannot properly solve the false-selection and the miss-selection caused by intrinsic scattering and temporal characteristics of the pixel on the whole dataset. Pseudo-PS and quasi-PS are first introduced into the high-quality pixel selection of GB-SAR interferometry, and a temporal selection method (TSM) is proposed to avoid the miss-selection of quasi-PS. Then, in order to achieve appropriate pixel density and high-quality pixels for deformation monitoring, a high-quality interferometric pixel selection method based on pseudo- and quasi-permanent scatterers analyses is proposed, which is a joint processing technique combining spatial variant apodization with TSM. Finally, the effectiveness of the proposed high-quality pixel selection method is validated by the field measurements of GB-SAR data.

A New Nonlocal Method for Ground-Based Synthetic Aperture Radar Deformation Monitoring

Ground-based synthetic aperture radar (GBSAR) interferometry offers an effective solution for the monitoring of surface displacements with high precision. However, coherence estimation and phase filtering in GBSAR interferometry is often based on a rectangular window, resulting in estimation biases and resolution loss. To address these issues, conventional nonlocal methods developed for spaceborne synthetic aperture radar are investigated with GBSAR data for the first time. Based on investigation and analysis, an efficient similarity measure is proposed to identify pixels with similar amplitude behaviors and a comprehensive nonlocal method is presented on the basis of this concept with the aim of overcoming current limitations. Pixels with high similarity are identified from a large search window for each point based on a stack of GBSAR single look complex images. Coherence is calculated based on the selected sibling pixels and then enhanced by the second kind statistic estimator. Nonlocal means filtering is also performed based on the sibling pixels to reduce interferometric phase noise. Experiments were conducted using short- and long-term GBSAR interferograms. Qualitative and quantitative analyses of the proposed nonlocal method and other existing techniques demonstrate that the new approach has advantages in terms of coherence estimation and phase filtering capability. The proposed method was integrated into a complete GBSAR small baseline subset algorithm and a time series analysis was achieved for two stacks of data sets. Considered alongside experimental results, this successful application demonstrates the feasibility of the proposed nonlocal method to facilitate the adoption of GBSAR for deformation monitoring applications.

Tomographic Imaging of Fjord Ice Using a Very High Resolution Ground-Based SAR System

This paper presents new experimental results of 3-D imaging using tomographic techniques over a snow covered sea ice medium, sensed with an X-band radar system. The available data are from a ground-based synthetic aperture radar data collection campaign carried out over Kattfjord, Tromso, Norway. Direct imaging of the vertical structures of the radar reflectivity of the snow and sea ice layers is achieved by focusing the signal from a 2-D synthetic array in the 3-D space. The effect of a change in propagation velocity of the wave inside the considered medium is investigated in the focusing process, and the tomograms are effectively corrected for this effect. The distribution of the scattering contributions in the vertical direction reveals a strong response from the sea ice cover. Tomograms at two different polarizations are investigated and compared. The results and the interpretations are also supported by the simulated data from the same system.

Ground-Based SAR Wide View Angle Full-Field Imaging Algorithm Based on Keystone Formatting

The interest of applying ground-based synthetic aperture radar (GB-SAR) in terrain displacement monitoring has increased for decades. Due to the large illuminating coverage and suitable working bandwidth, GB-SAR can achieve multitemporal surface deformation maps of the entire terrain with high spatial resolution and submilimetric accuracy. Unfortunately, focusing GB-SAR data in an efficient way is accompanied with a series of complicated engineering problems and should be solved. In this paper, a novel imaging algorithm tailored for GB-SAR data is proposed. First, the main characteristics of GB-SAR data are analyzed, and a signal model is then built up. Second, corresponding to this model, an imaging algorithm is put forward, which is based on keystone formatting and range-Doppler domain blocking. This algorithm is phase-preserving and can be widely applied in near-field, far-field, and wide view angle scenarios. Moreover, its computational cost is quite low, which includes only one time of linear interpolation and several times of fast Fourier transforms and complex multiplications. Finally, the algorithm is extensively validated both with numerical simulations and real GB-SAR data.

Efficient Wavenumber Domain Focusing for Ground-Based SAR

We present an efficient focusing algorithm for synthetic aperture radar (SAR) data acquired in short bursts by a geometry with a large range spread, i.e., the case of ground-based (GB) SAR. The usual approach for focusing GB SAR data is the polar formatting algorithm, whose computational complexity, however, is quite relevant due to the nonseparability of the kernel. In this letter, we introduce a different format for the focused data, namely, the range-angular domain. Such format keeps the benefit of the polar format that samples data close to the resolution, but allows for the design of a separable kernel in the wavenumber domain. The proposed kernel exploits a modified Stolt interpolation and an efficient space-varying resampling. Results of processing on both simulated and real data are presented.

An Accurate Strategy for 3-D Ground-Based SAR Imaging

In this letter, an analytical description of the 2-D and 3-D imaging of ground-based synthetic aperture radar data is given. The ability of the 3-D imaging to separate targets along the elevation direction will also be shown, thus allowing their complete localization in space. The validation of the proposed method is done by exploiting simulated data.

A Fast and Accurate Far-Field Pseudopolar Format Radar Imaging Algorithm

A novel imaging algorithm to be used under the condition of having an image scene in the far field of a linear radar aperture is presented. This is an application scenario that is drastically different from those of spaceborne and airborne synthetic aperture radar (SAR) systems, which has not been properly addressed to date. The technique is particularly tailored for a stepped-frequency continuous wave (CW) or frequency-modulated CW radar. The radar aperture must be linear and can be formed either with a physical or synthetic array. With the suggested method, the radar reflectivity of the image scene is obtained through an interpolation-free series expansion, where only 2-D fast Fourier transforms of the frequency-domain backscatter data are required. The resulting image is sampled on a ldquopolarlikerdquo or pseudopolar grid, which is introduced to simplify the formulation. The main advantages of this method are its extremely low computational cost and the high accuracy of the resulting imagery. The technique is extensively validated both with numerical simulations and two ground-based SAR data sets. Last but not least, numerical simulations show that this technique can be used with an ultrawideband radar of 1 GHz of bandwidth.