High Synthetic Aperture Radar Research Articles

Synthetic aperture radar (SAR) detects large gas seeps in Alaska lakes

Reservoirs of 14C-depleted methane (CH4), a potent greenhouse gas, residing beneath permafrost are vulnerable to escape where permafrost thaw creates open-talik conduits. However, little is known about the magnitude and variability of this methane source or its response to climate change. Remote-sensing detection of large gas seeps would be useful for establishing a baseline understanding of sub-permafrost methane seepage, as well as for monitoring these seeps over time. Here we explored synthetic aperture radar’s (SAR) response to large sub-permafrost gas seeps in an interior Alaskan lake. In SAR scenes from 1992 to 2011, we observed high perennial SAR L-band backscatter (σ 0) from a ∼90 m-wide feature in the winter ice of interior Alaska’s North Blair Lake (NBL). Spring and fall optical imagery showed holes in the ice at the same location as the SAR anomaly. Through field work we (1) confirmed gas bubbling at this location from a large pockmark in the lakebed, (2) measured flux at the location of densest bubbles (1713 ± 290 mg CH4 m−2 d−1), and (3) determined the bubbles’ methane mixing ratio (6.6%), radiocarbon age (18 470 ± 50 years BP), and δ13CCH4 values (−44.5 ± 0.1‰), which together may represent a mixture of sources and processes. We performed a first order comparison of SAR σ 0 from the NBL seep and other known sub-permafrost methane seeps with diverse ice/water interface shapes in order to evaluate the variability of SAR signals from a variety of seep types. Results from single-polarized intensity and polarimetric L-band SAR decompositions as well as dual-polarized C-band SAR are presented with the aim to find the optimal SAR imaging parameters to detect large methane seeps in frozen lakes. Our study indicates the potential for SAR remote sensing to be used to detect and monitor large, sub-permafrost gas seeps in Arctic and sub-Arctic lakes.

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.

Very High Resolution Automotive SAR Imaging from Burst Data

This paper proposes a method for efficient and accurate removal of grating lobes in automotive Synthetic Aperture Radar (SAR) images. Grating lobes can indeed be mistaken as real targets, inducing in this way false alarms in the target detection procedure. Grating lobes are present whenever SAR focusing is performed using data acquired on a non-continuous basis. This kind of acquisition is typical in the automotive scenario, where regulations do not allow for a continuous operation of the radar. Radar pulses are thus transmitted and received in bursts, leading to a spectrum of the signal containing gaps. We start by deriving a suitable reference frame in which SAR images are focused. It will be shown that working in this coordinate system is particularly convenient since it allows for a signal spectrum that is space-invariant and with spectral gaps described by a simple one-dimensional function. After an inter-burst calibration step, we exploit these spectral characteristics of the signal by implementing a compressive sensing algorithm aimed at removing grating lobes. The proposed approach is validated using real data acquired by an eight-channel automotive radar operating in burst mode at 77 GHz. Results demonstrate the practical possibility to process a synthetic aperture length as long as up to 2 m reaching in this way extremely fine angular resolutions.

SAR-AD-BagNet: An Interpretable Model for SAR Image Recognition Based on Adversarial Defense

Although deep neural networks (DNNs) have achieved good results on some common datasets of synthetic aperture radar (SAR) automatic target recognition (ATR), DNNs are opaque and difficult to interpret, which limits its practical application. In view of this, many interpretable models have been proposed in recent years. However, the previous interpretable models only represent transparent network structures and cannot be called real “interpretable” models. We think that the interpretability of a model contains two meanings: on the one hand, the decision process of the model is transparent, and on the other hand, the decision-making basis of the model should be reasonable and conform to human cognition. For this reason, we propose a new SAR image recognition model based on adversarial defense, namely SAR-AD-BagNet. Not only does it have a transparent decision-making process, but it also has a more reasonable basis for decision-making. In addition, the model also has high SAR image recognition accuracy and strong adversarial robustness.

Design of Power Supply System for High Power SAR Satellite

Synthetic Aperture Radar (SAR) satellite power system has the characteristics of high power, pulse power supply and fast response speed. Based on the combination of domestic and foreign SAR satellite power supply structures, a SAR satellite power supply system scheme with a composite bus architecture is proposed in this paper. By adding battery discharge regulators (BDR) between the platform bus and the SAR load bus as a grid-connected controller, the SAR load bus can supply power to the platform load, and the solar array and battery pack can be used efficiently while achieving dual bus output. Aiming at the composite bus, this paper also proposes an efficient high-power filter technology. Finally, the ground test results verify the correctness of the scheme, which can meet the different power supply requirements of the SAR satellite stable and pulse load equipment.

High resolution C-band SAR backscatter response to peatland water table depth and soil moisture: a laboratory experiment

ABSTRACT Carbon storage and active carbon sequestration within peatlands strongly depend on water table depth and soil moisture availability. With increasing efforts to protect and restore peatland ecosystems, the assessment of their hydrological condition is highly necessary but remains challenging. Synthetic aperture radar (SAR) satellite observations likely offer an efficient way to obtain regular information with complete spatial coverage over northern peatlands. Studies have indicated that both radar backscatter amplitude and phase are sensitive to peatland condition. Very recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been reported as being capable of monitoring ground deformation patterns at the millimetre scale, which are a response to peatland hydrological condition. To further investigate the promise of SAR for peatland monitoring, a laboratory-based polarimetric C-band SAR system was used to acquire the dynamic radar behaviour of a 4 m (l) ×1 m (w) × 0.25 m (d) reconstructed peatland. A forced 4-month drought was introduced with very-high-resolution imagery taken every 2 hours, capturing details of the vertical backscatter patterning through the peat at the centimetric scale. The results showed a clear coherent response both in radar backscatter amplitude and phase to change in water table level and soil moisture. Similar responses were seen across all polarizations. Phase demonstrated a coherent and deterministic change across the experiment; the average differential phase increase across all polarizations was 118° for 17 cm of water table drawdown. Interpreted as the physical movement of the surface, this corresponded to 8.3 mm of surface subsidence. Both phase and amplitude changes were near-linear with changes in the water table depth; amplitude showed a correspondingly strong concomitant mean decrease of 7 dB across all polarizations during the experiment. The results demonstrate the close sensitivity of radar backscatter to hydrological patterns in a peatland ecosystem. The phase result, in particular, strongly supports the notion that differential phase from satellites can be utilized to measure ground deformation as a proxy for the hydrological state.

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.

Analysis of Hypersonic Platform-Borne SAR Imaging: A Physical Perspective

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.

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

There is a data gap in our current knowledge of the geospatial distribution, type and extent of C rich peatlands across the globe. The Pastaza Marañón Foreland Basin (PMFB), within the Peruvian Amazon, is known to store large amounts of peat, but the remoteness of the region makes field data collection and mapping the distribution of peatland ecotypes challenging. Here we review methods for developing high accuracy peatland maps for the PMFB using a combination of multi-temporal synthetic aperture radar (SAR) and optical remote sensing in a machine learning classifier. The new map produced has 95% overall accuracy with low errors of commission (1–6%) and errors of omission (0–15%) for individual peatland classes. We attribute this improvement in map accuracy over previous maps of the region to the inclusion of high and low water season SAR images which provides information about seasonal hydrological dynamics. The new multi-date map showed an increase in area of more than 200% for pole forest peatland (6% error) compared to previous maps, which had high errors for that ecotype (20–36%). Likewise, estimates of C stocks were 35% greater than previously reported (3.238 Pg in Draper et al. (2014) to 4.360 Pg in our study). Most of the increase is attributed to pole forest peatland which contributed 58% (2.551 Pg) of total C, followed by palm swamp (34%, 1.476 Pg). In an assessment of deforestation from 2010 to 2018 in the PMFB, we found 89% of the deforestation was in seasonally flooded forest and 43% of deforestation was occurring within 1 km of a river or road. Peatlands were found the least affected by deforestation and there was not a noticeable trend over time. With development of improved transportation routes and population pressures, future land use change is likely to put South American tropical peatlands at risk, making continued monitoring a necessity. Accurate mapping of peatland ecotypes with high resolution (<30 m) sensors linked with field data are needed to reduce uncertainties in estimates of the distribution of C stocks, and to aid in deforestation monitoring.

High Speed Maneuvering Platform Squint TOPS SAR Imaging Based on Local Polar Coordinate and Angular Division

This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm.

High squint multichannel SAR imaging algorithm for high speed maneuvering platforms with small-aperture

For high speed maneuvering platforms, multichannel synthetic aperture radar (SAR) can realize wide-swath imaging more flexibly at a high squint. In this mode, the signal reconstruction and imaging is a challenging task because the direction of the channel array vector is time-variant and inconsistent with the radar velocity vector. In this paper, the properties of space time spectrum are analyzed in detail at first. It is found that the space time spectrum of the signal is irregular, in which the space time spectral lines are nonlinear and there is a massive Doppler spectrum shift. Therefore, a range-dependent signal reconstruction method based on space time spectrum correction is proposed to obtain the unambiguous Doppler spectrum. For wide-swath data processing, an improved Omega-K approach based on time domain spectrum compression is further proposed to obtain a well-focused image. A modified Stolt mapping is used to address the range variations of range cell migration (RCM). Subsequently, a time domain spectrum compression function is used to eliminate the time domain aliasing of small-aperture data without zero-padding. Simulation results and real data processing are presented to validate the proposed algorithm.

A Modified Range Model and Doppler Resampling Based Imaging Algorithm for High Squint SAR on Maneuvering Platforms

There are two technical difficulties to overcome before obtaining a well-focused image from high squint (HS) synthetic aperture radar (SAR) with constant acceleration. One is effective range modeling and the other is the correction of space-variant (SV) Doppler parameters. Based on the imaging characteristics analysis, an orthogonal expansion range model (OERM) is proposed which can handle the coordinate rotation caused by range walk correction (RWC). Then a modified spectral analysis (SPECAN) with the Doppler resampling method is designed to correct the SV Doppler parameters. Finally, the proposed algorithm is verified by both simulated and real SAR data. Meanwhile, it shows an improvement in azimuth focusing quality over the reference one.

Improved range model based on parameter estimation for high resolution and high squint LEO SAR

Due to the curve of the LEO synthetic aperture radar (SAR) orbit and the rotation of the earth, the high resolution imaging for LEO SAR is always facing the problem of obtaining accurate range history. Meanwhile, with the increase of squint angle, it will take longer azimuth time to meet the same high resolution. In this paper, an improved hyperbolic range equation (IHRE) is presented to model the accurate range history for high squint LEO SAR. First, the state vectors of satellite are used to estimate the Doppler parameters. Then, the improved hyperbolic range equation is recovered based on the estimation of the Doppler parameters. Compared with other existing methods, the superiority of IHRE is more precise in the condition of high squint LEO SAR which can provide sufficient azimuth time for high resolution imaging. The simulation and analysis validate the precision of the proposed range history model.

COSMO-SkyMed SAR for Detection and Monitoring of Archaeological and Cultural Heritage Sites

Synthetic aperture radar (SAR) imagery has long been used in archaeology since the earliest space radar missions in the 1980s. In the current scenario of SAR missions, the Italian Space Agency (ASI)’s COnstellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed) has peculiar properties that make this mission of potential use by archaeologists and heritage practitioners: high to very high spatial resolution, site revisit of up to one day, and conspicuous image archives over cultural heritage sites across the globe. While recent literature and the number of research projects using COSMO-SkyMed data for science and applied research suggest a growing interest in these data, it is felt that COSMO-SkyMed still needs to be further disseminated across the archaeological remote sensing community. This paper therefore offers a portfolio of use-cases that were developed in the last two years in the Scientific Research Unit of ASI, where COSMO-SkyMed data were analysed to study and monitor cultural landscapes and heritage sites. SAR-based applications in archaeological and cultural heritage sites in Peru, Syria, Italy, and Iraq, provide evidence on how subsurface and buried features can be detected by interpreting SAR backscatter, its spatial and temporal changes, and interferometric coherence, and how SAR-derived digital elevation models (DEM) can be used to survey surface archaeological features. The use-cases also showcase how high temporal revisit SAR time series can support environmental monitoring of land surface processes, and condition assessment of archaeological heritage and landscape disturbance due to anthropogenic impact (e.g., agriculture, mining, looting). For the first time, this paper provides an overview of the capabilities of COSMO-SkyMed imagery in StripMap Himage and Spotlight-2 mode to support archaeological studies, with the aim to encourage remote sensing scientists and archaeologists to search for and exploit these data for their investigations and research activities. Furthermore, some considerations are made with regard to the perspectives opened by the upcoming launch of ASI’s COSMO-SkyMed Second Generation constellation.

Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

New Approaches for Robust and Efficient Detection of Persistent Scatterers in SAR Tomography

Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches.

Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas

Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.

Dynamic Range Reduction of SAR Image via Global Optimum Entropy Maximization With Reflectivity-Distortion Constraint

The visualization of synthetic aperture radar (SAR) images plays a critical role in remote sensing applications. To effectively obtain the image suitable for human observation, this paper introduces a new SAR image visualization algorithm to map the high dynamic range SAR amplitude values to low dynamic range displays via reflectivity distortion preserved entropy maximization. Its designed objective is to present the maximal amount of information content in the displayed image, and being optimal in an information theoretical sense, as well as restricting the upper bound of the reflection distortion caused by tone mapping. The resulting optimization problem can be graph theoretically modeled as a $K$ -edges maximum weight path problem in a directed acyclic graph, and it can be solved efficiently by dynamic programming in real time. Empirical evidences are provided to demonstrate the superior visual quality obtained by our new visualization technique.

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.

Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (PD) and probability of false alarm (PFA), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring.