TomoSAR Techniques Research Articles

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

A deterministic descriptive regularization-based method for SAR tomography in urban areas

ABSTRACT In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas.

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.

A novel TomoSAR imaging method with few observations based on nested array

AbstractSynthetic aperture radar tomography (TomoSAR) baseline optimization method is capable of reducing system complexity and improving the temporal coherence of data, which has become important research in the field of TomoSAR. A nested TomoSAR technique is proposed, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co‐array through the nested array to increase the degrees of freedom of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co‐array, covariance matrix of the echo needs to be obtained. Therefore, a TomoSAR sparse reconstruction algorithm based on the nested array is proposed, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. The effectiveness of the proposed method through simulated and real data experiments is demonstrated. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of this proposed method have better anti‐noise performance and retain more image information.

Remote Monitoring of Civil Infrastructure Based on TomoSAR

Structural health monitoring and damage detection tools are extremely important topics nowadays with the civil infrastructure aging and deteriorating problems observed in urban areas. These tasks can be done by visual inspection and by using traditional in situ methods, such as leveling or using traditional mechanical and electrical sensors, but these approaches are costly, labor-intensive and cannot be performed with a high temporal frequency. In recent years, remote sensing has proved to be a very promising methodology in evaluating the health of a structure by assessing its deformation and thermal dilation. The satellite-based Synthetic Aperture Radar Tomography (TomoSAR) technique, based on the exploitation of a stack of multi-temporal SAR images, allows to remotely sense the movement and the thermal dilation of individual structures with a centimeter- to millimeter-level accuracy, thanks to new generation high-resolution satellite-borne sensors. In this paper, the effectiveness of a recently developed TomoSAR technique in assessing both possible deformations and the thermal dilation evolution of man-made structures is shown. The results obtained using X-band SAR data in two case studies, concerning two urban structures in the city of Naples (Italy), are presented.

TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach

SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3–5 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency.

Potential of P-Band SAR Tomography in Forest Type Classification

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

Forest Aboveground Biomass Estimation Using Multi-Features Extracted by Fitting Vertical Backscattered Power Profile of Tomographic SAR

Forest aboveground biomass (AGB), which plays an important role in the study of global carbon cycle, is one of the most important indicators in forest resource monitoring. Thus, how to estimate and map regional forest AGB quickly and accurately attracts more interests of researchers. Tomographic SAR (TomoSAR) is an advanced SAR technique developed in recent years, which has a wide range application in forest AGB estimation. In this paper, we proposed a multi-feature-based modeling method to estimate forest AGB by fitting backscattered power of TomoSAR vertical profile. The procedure of the proposed method includes four parts: (1) Processing TomoSAR data to obtain the backscattered power of vertical profile. (2) Fitting the backscattered power of the vertical profile. (3) Analyzing the fitted backscattered power distribution characteristic of the vertical profile. (4) Extracting the TomoSAR vertical profile features according to the forest AGB measurement factors based on the dendrometry theory. In this paper, we proposed two new features like the forest average height weighted by backscattered power (BPFAH) and the total length of the backscattered power curve (LBPC) as supplement features to estimate forest AGB by TomoSAR technique. We also used the traditional TomoSAR features including backscattered power at specific height layer of vertical power profile (BPV) and forest average height (FAH) for AGB estimation. After the feature selection, the selected features and the ground field data of the forest AGB were used for regression and modeling. Then the forest AGB was estimated and the accuracy was validated. The results showed that the accuracy of proposed method is 90.73%, and RMSE is 42.45 t/ha. Finally, we discussed the performance of our proposed method compared with traditional methods.

Optimal data acquisition in multi‐pass geosynchronous SAR tomography

Geosynchronous SAR tomography (GEO TomoSAR) techniques exploit multi-pass SAR acquisitions of the same scene, taken with slightly different view angles, and allow generating fully 3D images. Compared to low earth orbit (LEO) SAR case, GEO SAR has the advantages of shorter revisit time period and wider coverage, which can greatly shorten the data acquisition time, improve the coherence of collected data and effectively improve the 3D imaging accuracy. However, GEO SAR has curved trajectories and un-parallel repeated trajectories, which introduce the along-track component of the spatial baseline. The obtained GEO TomoSAR data based on the data acquisition method using in LEO TomoSAR have the significant rotation-induced decorrelation. Thus, the imaging performance in the elevation is severely degraded. In this paper, we first analyse the feasibility of GEO TomoSAR to achieve 3D imaging. In view of the special issues existing in GEO TomoSAR, we adopt an optimal minimal rotation-induced decorrelation data acquisition method to obtain repeated trajectories data, which can effectively improve the coherence between the collected data. Then, performance of imaging in elevation was analysed based on the spatial baseline distribution. Finally, the feasibility and validity of GEO TomoSAR techniques are validated through computer simulation.

An Empirical Study on the Impact of Changing Weather Conditions on Repeat-Pass SAR Tomography

Researches carried out in the last years have shown that the use of P-band SAR tomography (TomoSAR) largely improves the retrieval of the above-ground biomass (AGB) in tropical forests, providing a most encouraging element toward the systematic employment of TomoSAR techniques in the frame of the upcoming spaceborne mission BIOMASS. All of these researches were carried out using campaign data acquired in a single day, and under stable (mostly sunny) weather conditions. The impact of temporal decorrelation was considered in the literature by analyzing ground-based Radar data from the TropiSCAT campaign (Paracou, French Guiana), and found not to be a show-stopper for BIOMASS tomography. Yet, the validity of this analysis was limited to sunny days only. Accordingly, a precise assessment of the impact of changing weather conditions on TomoSAR is currently missing. The aim of this paper is to provide a first experimental element to fill this gap. To do this, data from the TropiSCAT archive are reprocessed to mimic BIOMASS repeat-pass tomography. Since BIOMASS tomography will be implemented by taking seven acquisitions with a revisit time of three days, we form tomograms by taking two TropiSCAT antennas every three days (and three antennas on the last day), which means that any single tomogram is actually obtained by mixing seven different days and under different weather conditions. The quality of tomographic imaging is then assessed by evaluating the observed backscattered power fluctuations in the tomogram time series. While imaging quality is observed to degrade by mixing different days, the resulting temporal variations of the backscattered power in the canopy layer are within 1.5-dB rms in cross polarization. For this forest site, this error is translated into an AGB error of about 50–100 t/ha, which is 20% or less of forest AGB.

EVALUATION OF AIRBORNE L- BAND MULTI-BASELINE POL-INSAR FOR DEM EXTRACTION BENEATH FOREST CANOPY

Abstract. DEM beneath forest canopy is difficult to extract with optical stereo pairs, InSAR and Pol-InSAR techniques. Tomographic SAR (TomoSAR) based on different penetration and view angles could reflect vertical structure and ground structure. This paper aims at evaluating the possibility of TomoSAR for underlying DEM extraction. Airborne L-band repeat-pass Pol-InSAR collected in BioSAR 2008 campaign was applied to reconstruct the 3D structure of forest. And sum of kronecker product and algebraic synthesis algorithm were used to extract ground structure, and phase linking algorithm was applied to estimate ground phase. Then Goldstein cut-branch approach was used to unwrap the phases and then estimated underlying DEM. The average difference between the extracted underlying DEM and Lidar DEM is about 3.39 m in our test site. And the result indicates that it is possible for underlying DEM estimation with airborne L-band repeat-pass TomoSAR technique.

Urban Area Tomography Using a Sparse Representation Based Two-Dimensional Spectral Analysis Technique

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power for elevation, to discriminate scatterers with differences in location in the same range-azimuth cell. However, they cannot provide sufficient range resolution power to discriminate these scatterers by joining different spectra to a wider range spectrum, which limits the resolution power of TomoSAR. Therefore, in this paper, we proposed using a compressive sensing (CS) technique to reconstruct range and elevation signals from multi-baseline SAR images. This TomoSAR method not only retrieves the vertical signals but also improves the resolution in the range, which helps improve the resolution power of the TomoSAR technique. We present the theory of the CS-based two-dimensional TomoSAR technique and compare it to the CS-based one-dimensional TomoSAR technique. The excellent resolution power and scatterer localization accuracy of this novel technique are demonstrated using simulations and real data obtained by RADARSAT-2 in Lanzhou, China.

Single and Multipolarimetric P-Band SAR Tomography of Subsurface Ice Structure

In this paper, first results concerning the characterization of the subsurface of ice sheets and glaciers through single and multipolarization synthetic aperture radar (SAR) tomography (TomoSAR) are illustrated. To this aim, the processing of data acquired in the framework of the European Space Agency IceSAR 2012 campaign is discussed. IceSAR 2012 was conceived so as to support the secondary objectives of the future Earth Explorer mission BIOMASS, which will be a SAR instrument with media penetration capabilities due to the use of the P-band frequency. In this regard, a tomographic study of ice was motivated by the fact that cryospheric remote sensing is of fundamental importance in order to understand more in depth the morphology and the dynamic processes regulating ice sheets. The main objective of the tomographic experiment of the campaign herein discussed was indeed to assess the capability of P-band SAR to retrieve any information about ice subsurface structure. Imaging has been achieved through TomoSAR techniques, applied to airborne multibaseline data acquired in the southwest of Greenland. Different imaging approaches are compared, and the main results achieved are presented: It is found that scattering in the upper layers of glacial subsurface can be achieved up to an extent of about 20-60 m, conditional on the different types of glaciological zone observed. Moreover, clear morphological structures have been found beneath the ice surface at one of the investigated sites.

Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.