The digital elevation model (DEM) reconstruction accuracy of single-channel interferometric synthetic aperture radar (SC-InSAR) is limited by the SAR side-looking imaging geometry, decorrelations, phase unwrapping (PU), and so on. With the availability of increasing InSAR data, to overcome the limitations of SC-InSAR, a multi-source InSAR DEM reconstruction framework based on a complexity factor is proposed in this article. To simultaneously take the effects of noise level and terrain slope into account, a complexity factor for each interferometric pair is constructed. Next, to reduce the PU failure rate for each pair, this factor is used to guide the two-stage programming approach (TSPA) PU method. Then, to avoid the adverse effects of PU failure on elevation fusion, unreliable pixels of each pair are detected by exploiting the complexity factor. Finally, after multiple elevations from different side-looking directions are obtained, the elevation-weighted fusion is performed to reconstruct the final DEM in the map projection coordinate system. Experimental results on real multi-source InSAR data demonstrate that the complexity factor can effectively guide the steps of TSPA PU, detection of unreliable pixels, and elevation-weighted fusion in the proposed framework, thereby improving the DEM reconstruction accuracy for mountainous areas with complex and steep terrain.

Tomographic SAR (TomoSAR) at low frequency, i.e., P/L band, has become a promising tool for forest structure study. Forest canopy height and underlying topography are two of the most important parameters one can estimate using TomoSAR technique. One simple way to estimate these two parameters is to detecting the peaks of the tomographic profile, which, however, can lead to large biases due to complicated forest structure, sidelobes or insufficient TomoSAR resolution. Polarimetric TomoSAR (Pol-TomoSAR) provides a solution to this by exploring the polarimetric diversity to separate the ground and canopy components and then conduct independent TomoSAR analysis. However, Pol-TomoSAR technique suffers from low ground-to-volume ratio (GVR), which often leads to unsuccessful ground and canopy separation. To mitigate this propblem, in this paper, we provide a deep-learning solution to ground and canopy height estimation from 3D tomographic profile through the identification of the patterns of ground and canopy peaks. A 3D U-net model is introduced in our solution to grasp as much three-dimensional characteristics of the tomographic profile as possible. Moreover, our model can be well trained using only synthetic TomoSAR dataset, making it easy to implement when we don’t have enough real data with LiDAR references. The proposed method is validated on P-band real TomoSAR dataset from multiple test sites in AfriSAR campaign, showing that it can achieve more accurate ground and canopy height estimation than the state-of-the-art Pol-TomoSAR techniques. The maximum RMSE improvement reaches as high as 66.4% and 63.2% for ground and canopy top height, respectively.

The coherence level and number of selected interferometric pairs are directly related to the interferometric synthetic aperture radar (InSAR) phase estimation accuracy. In the Multi-Channel InSAR and Multi-Temporal InSAR, it is essential to select high-coherence interferometric pairs and remove low-coherence ones from the massive SAR singlelook complex (SLC) image data. The existing selection method, which based on basic coherence spectral clustering, may become increasingly computationally intensive when processing real-time data. To make a trade-off between the calculation cost and selection accuracy, a novel SAR interferometric pairs selection method based on incremental coherence spectral clustering is proposed. Experimental results demonstrate that the proposed method, which involves selecting a representative SAR SLC image for each cluster rather than re-constructing the adjacency matrix and re-estimating the number of clusters, can yield similar interferometric pairs selection results at a reduced computational cost.

Baseline errors is the main error source of airborne multi-baseline SAR tomography. P-band SAR can penetrate into deep vegetation layer even in tropical forests and therefore offers huge potentials in forest structure study. This paper introduces a novel method to estimate and compensate these baseline errors based on small baseline subset interferometric network, which is, compared to the existing methods, (1) less prone to heavy decorrelation noise induced by forest volume scattering and (2) easy to implement without pixel-by-pixel optimization. Numerical experiments conducted on real airborne P-band multi-baseline SAR dataset demonstrate that the proposed method can effectively estimate and correct the baseline errors.

Multibaseline interferometric synthetic aperture radar (MB-InSAR) has attracted widespread attention as it can improve the measurement accuracy of elevation or deformation by exploring baseline diversity. However, the interferometric phase is normally contaminated by phase noise, which directly affects the measurement accuracy. In this article, an MB-InSAR interferometric phase noise suppression method based on BM4D (MBInSAR-BM4D) is proposed. To increase the number of similar cuboids for grouping, a topographic phase compensation strategy is introduced, which can reduce fringe density in complex topography. In addition, to accurately select similar cuboids from residual interferometric phase stack and collect them into 4-D groups, the generalized likelihood-ratio (GLR) test is applied, in which the observed amplitude, coherence, and phase are utilized simultaneously for improving the grouping accuracy. After performing collaborative filtering and aggregation on 4-D groups, the MB-InSAR interferometric phase stack noise suppression results are obtained by adding the reference phase back to the corresponding filtered residual interferometric phase. All interferometric phases are fully exploited to facilitate MB-InSAR phase stack filtering performance enhancement. Experimental results on both the simulated and real MB-InSAR data demonstrate that the proposed MBInSAR-BM4D provides superior noise suppression and fringe detail preservation for the MB-InSAR interferometric phase stack.

Baseline error correction is critical for airborne synthetic aperture radar (SAR) tomography as the actual flight trajectory often deviates from the designed one due to turbulence, which may lead to large sidelobes or even complete defocusing in the tomograms. Current baseline error correction methods, however, are susceptible to heavy decorrelation noise. To mitigate the adverse effect of decorrelation noise, in this article, a novel method for airborne SAR tomography baseline errors correction driven by small baseline interferometric phase is proposed. In this method, a novel mathematical model that relates interferometric phase to the baseline error differences is first derived; then, a small baseline interferometric pairs selection strategy is employed to estimate the baseline error differences through an alternate iterative algorithm, and finally, the baseline errors are obtained through accumulating summation of the baseline error differences. The use of small baseline interferograms can avoid the phase linking processing and thereby greatly alleviate the heavy decorrelation effect. Both simulated and real airborne P-band SAR tomography experiments have demonstrated that the proposed method can achieve more accurate and robust estimation of baseline errors and is more tolerant to decorrelation noise than the well-known phase center double localization (PCDL) method.

Polarimetric synthetic aperture radar (SAR) tomography (Pol-TomoSAR) can be used for global forest digital terrain model (DTM) and canopy height model (CHM) mapping with high spatial and temporal resolution at low economic cost. However, the performance of DTM and CHM inversion in current Pol-TomoSAR methods is often compromised when the ground-to-volume ratio (GVR) is low, which usually happens in complicated terrain where large negative slope angles are commonly present, or in dense tropical forest where the ground visibility is low due to strong attenuation by the dense vegetation layer. In this work, a novel method for the separation of ground and volume scattering, aiming at robust and accurate DTM and CHM inversion in dense tropical forest and complicated terrain, is proposed. By fully exploiting multibaseline polarimetric SAR data, the proposed method can perform a more effective separation of ground and volume scattering. Subsequently, by applying the SAR tomography technology on the separated ground and volume scattering, the proposed method can retrieve accurate DTM and CHM information even at low GVR areas. Numerical experiments conducted on both simulated data and P-band airborne F-SAR data show that, compared to the most commonly used two-component algebraic synthesis method, the proposed one has a much better performance in terms of separation of ground and volume scattering. Furthermore, the inversed DTM and CHM present better agreement with light detection and ranging (LiDAR) measurements, especially in large negative slope angle terrain where the GVR is rather low.

Integrated sensing, computing and communications (ISC2) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC2system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop Distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

To achieve accurate interferometric synthetic aperture radar (SAR) phase estimation, it is essential to select appropriate high-coherence interferometric pairs from massive SAR single-look complex (SLC) image data. The selection should include as many high-coherence interferometric pairs as possible while avoiding low-coherence pairs. By combining coherence and spectral clustering, a novel selection method for SAR interferometric pairs is proposed in this paper. The proposed method can be adopted to classify SAR SLC images into different clusters, where the total coherence of interferometric pairs in the same cluster is maximized while that among the different clusters is minimized. This is implemented by averaging the coherence matrices of representative pixels to construct an adjacency matrix and performing eigenvalue decomposition for estimating the number of clusters. The effectiveness of the proposed method is demonstrated using 33 TerraSAR-X and 38 dual-polarization Sentinel-1A data samples, yielding improved topography and deformation monitoring results.

Multitemporal synthetic aperture radar interferometry (MTInSAR) is an efficient geodetic tool for Earth surface displacement measurement, and the polarimetric capability of current and upcoming SAR satellites offers a new opportunity to further improve MTInSAR phase series estimation. However, none of the existing estimators for multipolarimetric MTInSAR phase series of distributed scatters (DSs) is derived under the minimum root-mean-square error (RMSE) criterion. In this work, a maximum likelihood estimator for multipolarimetric phase linking (MLE-MPPL) is proposed and the corresponding Cramer–Rao lower bound (CRLB) is also derived by modeling the polarimetric interferometric coherence matrix as the Kronecker product of polarimetric coherence matrix and interferometric coherence matrix. In addition, a new metric called Pol-detR is proposed for the performance evaluation of multipolarimetric MTInSAR phase series estimation in practical scenarios where the RMSE is not feasible any more. The experimental results based on both simulated and real data show that the proposed MLE-MPPL achieves the best estimation performance and is more robust against interchannel interference than existing methods.

Baseline is an essential parameter in Interferometric SAR (InSAR). It's directly related to the estimation accuracy of elevation, so precise baseline estimation is required. Aiming at the existing baseline estimation methods based on interferometric fringe frequency, this paper proposes an improved baseline estimation method based on least square. Compared with the existing methods, the proposed method can fully exploit the potential of the interferogram data by applying the least square method to solve the formula between fringe frequency and baseline parameter to achieve baseline estimation. The simulated results show that the proposed method has improved performance in both accuracy and robustness for InSAR baseline estimation.