The Hongtu-1 (HT-1) synthetic aperture radar (SAR) system is the first spaceborne single-pass multibaseline (MB) interferometric SAR (InSAR) system based on four HT-1 SAR satellites flying in a cartwheel formation. This setup includes three secondary satellites that function as receive-only units to create a compact multistatic SAR system. The primary objective of the HT-1 mission is to generate a consistent global digital elevation model (DEM) at 1:50000 scale. In addition, the HT-1 mission will feature novel SAR imaging technology demonstrations. On March 30, 2023, four HT-1 satellites were successfully launched and started to provide spaceborne radar data services to users. This article provides a detailed description of the HT-1 MB InSAR system, including the SAR performance, the cartwheel formation designed for multistatic SAR data collection with desired baselines, and the uninterrupted synchronization link. The interferometric performance is thoroughly analyzed. With the recorded data, imaging and interferometric processing procedures are introduced, and the capabilities of single-pass MB InSAR DEM generation are demonstrated. Compared with those of ICESAT, the height errors are less than 2 m in flat terrain and less than 5 m in mountainous terrain. Moreover, the resolution and swath of multisatellite mosaic imaging are 3 m and 80 km, respectively. The repeat-pass differential InSAR measurement for surface deformation monitoring is also included.

Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise three-dimensional (3D) forest vertical structure information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3D reconstruction, which is time-consuming and low-efficiency for forest vertical structure mapping. To address these limitations, this paper proposes a novel micro-stack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P- and L-band. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improvingR2from 0.443 to 0.628, mean absolute error (MAE) from 0.779 m to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 m to 0.771 m. Additionally, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the L-band results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.

Airborne Synthetic Aperture Radar (SAR) Tomography (TomoSAR) technology facilitates the extraction of three-dimensional (3D) information of target scatterers. However, phase screens induced by radar platform trajectory errors causes TomoSAR defocusing, which adversely affects the accuracy of the forest vertical structure retrieval. Additionally, the phase screens exhibit space-variant characteristics, which significantly degrade the calibration performance estimated by traditional phase gradient autofocus (PGA). This paper proposes an improved phase calibration method synthesizing unconstrained optimization model and weighted PGA (WPGA), which effectively address the above challenges. Firstly, the SAR data stack is segmented into multiple subareas by assuming that phase screens are space-invariant within each small area, which reduces complexity and improves computational efficiency. Secondly, a WPGA method synthesizing the scatterer heights derived from an unconstrained optimization model is proposed to estimate the phase screens. Simulation experiments are conducted to validate the effectiveness of the proposed phase calibration method. Furthermore, the full-polarization SAR data stacks acquired by the BorTomoSAR campaign are used for tomographic focusing analysis. Experimental results demonstrate that the proposed method accurately estimates the phase screens at the P, L, and S bands, providing a efficient solution for the forest vertical structure retrieval.

The Hongtu-1 (HT-1) Synthetic Aperture Radar (SAR) constellation is the first in-orbit spaceborne single-pass multi-baseline interferometric SAR (InSAR) system. The system has the ability to conduct high-resolution earth observation and high-precision, high-efficiency terrain surveying. The highest resolution of the system is better than 0.5 m, and it has a 1:50000 scale global digital elevation model (DEM) and digital surface model (DSM) surveying capability. This paper provides a basic introduction to the HT-1 constellation, and demonstrates the advantages of single-pass multi-baseline InSAR results and its advantages over steep area.

Interferometric Synthetic Aperture Radar (InSAR) shift measurement plays a key role in image coregistration and absolute phase measurement and has significant applications in the InSAR processing workflow. However, the current shift measurement algorithms are limited by the relative bandwidth of the SAR system, resulting in low resolution and accuracy. SSENet is a recent InSAR shift measurement approach that utilizes deep learning to address these issues to some extent. This paper proposes a calibration method for SSENet, which introduces a lightweight neural network designed to refine the marginally biased output shifts. Furthermore, we demonstrate the performance of the refined SSENet algorithm and its potential in assisting phase unwrapping using LSAR-01 bistatic Synthetic Aperture Radar (SAR) data.

The short-revisit synthetic aperture radar (SAR) provides a large amount of data for interferometric SAR (InSAR) application. Sequential estimator can efficiently utilize the newly acquired SAR images to dynamically monitor surface deformation without reprocessing those already processed images. Sequential estimator uses statistically homogeneous pixels (SHPs) to improve the phase quality. However, the selection of SHPs mainly focuses on SAR images acquired before initial processing, without considering the potential failure of SHPs in the newly acquired SAR images. In fact, the destruction of the homogeneity of SHPs in newly acquired data is a common occurrence, especially in some changed areas, such as the urban construction and the seasonal changes in farmland. The inaccurate identification of the SHPs can compromise the quality of phase filtering, ultimately affecting the precision of deformation results. In this letter, we proposed a novel SHP selection algorithm named SHP update (SHPU) algorithm. SHPU detects the SHPs whose homogeneity gets destroyed in the new acquisitions and updates those SHPs. Experimental results demonstrate that SHPU can detect approximately over 70% of incorrect SHPs and reselect them. The required time of SHPU is only 30% of the time needed for re-estimation using newly acquired data.

Multibaseline (MB) synthetic aperture radar inter-ferometry (InSAR) is capable of reconstructing steep terrain height profiles, and MB phase unwrapping (PU) is one of the most critical and challenging steps in its processing chain. Existing MB PU algorithms usually suffer from poor noise robustness or low computational efficiency. In this work, we propose a fast and robust MB PU algorithm, including three main steps: 1) construct the triangulation network guided by the quality map; 2) obtain robust estimation of ambiguity number gradients based on the intrinsic relationship between interferograms and gradient regularization; 3) solve minimum cost flow problem with modified weights. The mean absolute errors of the height profiles constructed by the proposed method are 1.6m and 1.3m for simulated data and real data experiments respectively, verifying the effectiveness of the proposed method.

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) has been well-established for forest height estimation. However, employing mono-frequency SAR data for PolInSAR tree height inversion presents inherent limitations, posing challenges to ensure inversion accuracy. This article presents a novel method for the inversion of vegetation parameters using dual-frequency (DF) four-stage PolInSAR, aiming to address the limitations observed in mono-frequency inversion. By leveraging the differential penetration of vegetation across distinct frequency bands, this method facilitates the derivation of more precise volume-only coherence and ground phase information. Applying the DF four-stage PolInSAR method to a substantial dataset of simulation results identifies the optimal band combination as P- and L-band. Moreover, the band combination that yields the most significant enhancement in accuracy is determined to be L- and S-band. These simulation results inform the design of the DF full-polarization SAR system. Subsequently, airborne SAR data are acquired using this L- and S-band full-polarization airborne SAR system over the Saihanba Forest Farm in Hebei, China. Ground-based LiDAR measurements serve as reference values for the comparison of PolInSAR inversion results. The DF four-stage PolInSAR method has a 5.46% improvement in the inversion accuracy of airborne SAR data. Both simulation and airborne SAR data inversion outcomes demonstrate a significant enhancement in forest height inversion accuracy achieved through the DF four-stage PolInSAR method compared to the mono-frequency approach.

Stereo-radargrammetric shift estimation is an important part of interferometric synthetic aperture radar (InSAR) data processing. However, the presence of residual topographical phase poses a challenge to achieving accurate coherent shift estimation in future high-resolution InSAR measurement tasks. In this work, we present a two-stage multi-baseline InSAR stereo-radargrammetric shift joint estimation approach. Our proposed method reduces the influence of the residual topographical phase, even in cases where no prior information is available or with low resolution prior digital elevation models (DEMs). In addition, a topography model based on Brownian motion is used to analyze the effect of the residual topographical phase on the accuracy of the shift estimation.

Compared with the rapid development of single-frequency polarimetric SAR (PolSAR) image classification technology, there is less research on the land cover classification of multi-frequency PolSAR (MF-PolSAR) images. And the deep learning methods among them are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, this paper proposes the MF semantics and topology fusion (MF-STF) model based on semantic interaction and nonlocal topological structure to improve MF-PolSAR classification performance. During MF-STF optimization, the semantic information-based classification (SIC) and topological property-based classification (TPC) work collaboratively, not only fully leveraging the complementarity of bands, but also combining local and nonlocal spatial information to improve the discrimination of different categories. For SIC, the designed cross-band interactive feature extraction (CIFE) module is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. In TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, a MF weighted fusion (MFWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. Notably, its weights are adjusted based on the total model loss. The effectiveness of the proposed modules is proved by ablation experiments on three measured MF-PolSAR datasets. In addition, the comparative experiments show that MF-STF can achieve more competitive classification performance than some state-of-the-art methods.

The interferometric synthetic aperture radar (InSAR) image shift measurement technique is of great significance in processing high-precision digital elevation model (DEM) generation and deformation measurements. It can be used in steps such as image fine coregistration, interferometric phase unwrapping and absolute phase calibration in the InSAR processing flow without an external DEM. However, the shifts estimated by current methods are of low resolution and have high measurement noise, which may have adverse impacts on subsequent applications. In this paper, a lightweight, high-resolution and low-noise interferometric stereo-radargrammetric shift estimation network (SSENet) is proposed to solve the aforementioned problems. It introduces deep learning technology to the InSAR shift estimation task for the first time. We propose forming multiscale 3D coherence coefficient cubes by projecting the shift values of the images onto the third dimension and then using a 3D convolutional network for multiscale fusion and encoding, followed by decoding with linear layers. In addition, a dataset generation and augmentation scheme based on real data is designed for model training and evaluation. Several sets of real SAR images from different regions of the world were used to evaluate SSENet. Compared with the typical coherent cross-correlation approach, SSENet reduces the mean absolute error of the estimated shifts by approximately 79% while improving the resolution by a factor of 4×4, making it possible to restore the absolute interferometric phase. Finally, we demonstrate a stitching strategy for processing large-scale SAR images and discuss the multiple potential uses of SSENet in the InSAR processing chain.

An accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for the geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this article, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this article, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-Earth phase-based baseline refinement methods are made. The results indicated the superior accuracy and robustness of our method, especially in areas with higher relief and wider coverage.

Nonlinear frequency modulation (NLFM) signal can construct the signal’s power spectral density to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, can not accurately describe ionospheric dispersion effects faced by the NLFM signal due to the non-linear characteristic of the instantaneous frequency. Thus, an extended model is established in this paper to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation signals are compared. Afterwards, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this paper is used in the ground processing system of LT-1.

One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems.

Interferometric synthetic aperture radar (InSAR) is an efficient tool for global large-scale digital elevation model (DEM) generation. However, for steep terrain, the current approaches cannot stably reconstruct valid DEM products from a single-baseline InSAR image pair without an external reference DEM due to the influence of shadow/layover geometries, phase noise, and the Itoh condition limitation in the phase unwrapping (PU) process. In this article, a novel stereo-radargrammetry-assisted PU (SAPU) approach with no need for external auxiliary information is proposed to eliminate the constraint of the Itoh condition by exploiting the internal stereo-radargrammetric shifts. The method reduces the phase gradient in the interferogram and guides the PU process with automatically selected tie points. Notably, the current stereo-radargrammetry approaches will deteriorate to an incoherent state in steep terrain, hampering the reliability and accuracy of SAPU. Accordingly, we also propose an adaptive weighted subwindow-coherent stereo-radargrammetric shift estimation (AWS-CSE) method to improve the accuracy of subpixel shifts by introducing local topographic phase consistency in coherence estimation. We quantitatively validate the performance of the proposed methods based on the L-SAR 01 simulation data and three pairs of repeat-pass single-baseline Advanced Land Observing Satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) images from different areas, comparing the results with those of various traditional and deep-learning-based PU methods. The findings suggest that the proposed methods can generate accurate DEMs from single-baseline measurements in steep terrain while avoiding additional data acquisitions.

Dual-frequency polarimetric synthetic aperture radar (PolSAR) data can provide more information than single-frequency data, which can effectively improve classification accuracy. However, how to obtain sufficient and non-redundant feature representation from dual-frequency PolSAR data remains to be resolved. Besides, deep learning has shown good performance in PolSAR image classification, but it often requires a large number of labeled samples to participate in the training process, which is time-consuming and labor-intensive. In this paper, we propose a novel dual-frequency PolSAR image semi-supervised classification method that combines a dual-frequency joint feature learning (DFJFL) module with a cross label-information network (CLIN). First, the DFJFL module is developed based on the consistency and complementarity of dual-frequency data. It eliminates information redundancy by feature constraint loss function, and obtains compact dual-frequency joint feature representation. Subsequently, in order to avoid the influence of speckle noise, the proposed CLIN not only applies consistency regularization under network perturbation, but also uses the scattering mechanism of PolSAR data to find similar sample pairs to complete the consistency regularization under input perturbation, thereby achieving semi-supervised classification for PolSAR data. Experiments on four real dual-frequency PolSAR datasets verify that the proposed method can effectively extract dual-frequency PolSAR information, and make full use of unlabeled samples to improve classification accuracy. At the same time, compared with several related image classification algorithms, the proposed method could achieve the best performance.

Atmospheric artifacts and phase unwrapping errors have unfavorable effects on differential synthetic aperture radar interferometry (DInSAR) deformation monitoring. In this letter, we present an alternative small-baseline DInSAR inversion algorithm, velocity-constraint L1-norm minimization. The proposed algorithm improves the robustness of time series deformation estimation by combining a smoothing constraint and L1-norm minimization. The smoothing constraint can minimize temporal atmospheric artifacts, and the L1-norm minimization outperforms L2-norm minimization in the presence of phase unwrapping errors. The iteratively reweighted least square algorithm is employed to adjust the weights of DInSAR observations and smoothing constraints in L1-norm minimization. The proposed algorithm is validated using simulated data and TerraSAR-X data. The experimental results show that the proposed algorithm is suitable for the inversion of approximately linear deformation processes affected by both atmospheric artifacts and unwrapping errors.

In this paper, we extend the previously proposed Tomo-PSInSAR method to detect homogeneous objects in the urban environment. Tomo-PSInSAR integrates conventional persistent scatterer (PS) interferometry and multidimensional SAR tomography to monitor complex built environments [1]. It can jointly detect single and overlaid PSs by constructing a two-tier hierarchical network. Robust estimators (M-estimator and ridge estimator) are introduced to improve the robustness of estimation. To monitor the semi-artificial regions (e.g, pavements and small grassed lands) that are normally distributed scatterers (DSs) in SAR images [2], we analyze homogeneous pixels on the basis of Tomo-PSInSAR. Before estimating the geophysical parameters, we perform a two-sample Anderson-Darling test for the identification of statistically homogeneous pixels at the stage of interferometry. In the first-tier network, the most reliable PSs are identified and they will be used as reference points in the second-tier network. In the second-tier network, the geophysical parameters (e.g., height, deformation velocity) of overlaid PSs are estimated using tomographic imaging [3] and the geophysical parameters of DSs are estimated using the Capon-Beamforming algorithm [4]. The removal of atmospheric delay in the second-tier network is accomplished by subtracting the phase of adjacent PSs that are detected in the first-tier network. In this sense, the proposed integrated method as shown in Fig. 1 can jointly monitor single PSs, overlaid PSs, and DSs according to specific cases. TerraSAR-X/TanDEM-X images are used to validate this method. The results are shown in Fig. 2-4.

It has been proven that SqueeSAR technique has validated the potential to increase the density of measure points (MP) involving persistent scatter (PS) and distributed scatter (DS) candidate in nonurban areas. In SqueeSAR, DS candidate exhibiting high extended temporal coherence can be processed jointly with PS for deformation estimation after phase estimation by the phase triangulation algorithm (PTA). The PTA extracts phase values of DS by using all possible interferograms under the Gaussian scattering assumption. However, the statistics hypothesis, Gaussian random variable, is no more applicable, with radar resolution increasing. More precisely, it has been shown that clutter in high resolution SAR images can be modeled as a compound Gaussian process. In this letter, a modified approach to phase estimation of DS named MPTA is proposed based on the compound Gaussian model. Experiments on real data are presented to demonstrate the effectiveness of the proposed method.

Statistically Homogeneous Pixels (SHPs) selection is a significant step of multi-temporal interferometric synthetic aperture radar (InSAR) for Distributed Scatterers (DS). A series of studies namely, Anderson-Darling test (AD test) and its variants, have demonstrated their advantages. However, these algorithms have a similar drawback that they put little attention on the spatial amplitude distributions and cost too much time of processing. To solve the problem, this paper proposes a modified statistically homogeneous pixels selection algorithm (MoSHPS). It utilizes the amplitude values to get the prior information of the images through an unsupervised classifier, in order to guide the SHPs selection. It can improve the accuracy for SHPs selection, and promote the computing efficiency. In the end, results on a series of real TerraSAR-X datas, acquired over a Tianjin area, confirm the effectiveness of this algorithm.

Statistically homogeneous pixels (SHPs) are considerably significant in many interferometric applications, such as interferometric filtering, distributed scatterer selection, small baseline subset, and SqueeSAR processing. It is very important to achieve SHPs efficiently and accurately. Previous studies on SHPs' selection are based on spatial restrictions and likelihood ratio test, such as Lee filtering, Kolmogorov-Smirnov test, and Anderson-Darling test. However, these algorithms do not stand up to test for the spatial similarity hypothesis for complex terrains with a few images. To solve the problems, this letter proposes a modified SHPs' selection algorithm. It utilizes geometric distance and target features for reaching a priori information to help the similarity hypothesis tests. The proposed algorithm has been tested on simulated and real data to prove the improvements in terms of accuracy and computational efficiency.