Accurate sub-canopy terrain modeling is essential for applications such as forest hydrological analysis, ecosystem monitoring, and slope stability assessment. Existing PolInSAR-based DEM inversion methods are predominantly built upon the RVoG model, whose overly idealized assumptions about forest scattering severely limit their accuracy and robustness in practical scenarios. To address these limitations, this study focuses on the most critical stage of the interferometric processing chain—the nonlinear modeling of the interferometric ground phase. We propose CVPI-Net (Complex-valued Polarimetric Interferometry Network), a deep learning framework for precise phase reconstruction. CVPI-Net ingests complex-valued polarimetric interferometric features and employs a complex-extended Swin Transformer encoder to extract multi-scale spatial features. An Entropy-Gate Attention decoder is then designed to enhance spatial awareness and enable deep modeling of amplitude-phase coupling. Furthermore, to improve the robustness of phase unwrapping, we incorporate a Von Mises-based maximum likelihood loss into the phase regression process, jointly modeling confidence to provide explicit quality constraints. The effectiveness of the proposed framework was systematically evaluated using SAOCOM PolInSAR data, with airborne LiDAR-derived DTM serving as the reference. The results show that CVPI-Net achieves superior phase continuity and elevation accuracy; in terms of DEM inversion, it obtains an RMSE of 3.60 m, corresponding to a 68.3% reduction relative to the TanDEM-X-derived DEM baseline. CVPI-Net also outperforms a real-valued counterpart with the same architecture whose parameter count is approximately twice that of CVPI-Net. In addition, the experiments validate the effectiveness of the proposed confidence-aware loss in improving phase modeling quality and enhancing the stability of the unwrapping process. The code will be available at https://github.com/Liiiiiixs/CVPI-Net .

High-resolution optical satellite stereo image pairs have been a challenge for dense matching due to their characteristics, such as long baseline and significant occlusion. This letter proposes a semiglobal matching with optimized penalty and interpolation (OPI-SGM), which includes the segmental adaptive adjustment (SAA) for the penalty term and the dual-path interpolation (DPI) for invalid regions. We introduce$\gamma $as the maximum absolute gray value difference between pixels in the same plane and combine it with the logarithmic function to make SAA less sensitive to penalty adjustment in the aggregation function. DPI uses two paths to improve interpolation reliability and uses$\gamma $as a threshold to filter weighted valid disparities, reducing the number of erroneous disparities near dense building areas. Experimental results show that OPI-SGM outperforms other SGM variants. When processing new data, it produces more accurate disparity maps than two end-to-end matching networks, demonstrating the effectiveness of the proposed algorithm.

Radargrammetry is a widely used technique for generating high-resolution digital surface model (DSM). Traditionally, radargrammetry relies on two synthetic aperture radar (SAR) images acquired at different incident angles. However, in cases where multiple SAR images are available, traditional radargrammetry methods fail to exploit the complete information, resulting in suboptimal accuracy of the DSM. In this letter, we propose a novel stereo positioning feedback approach for multi-image radargrammetric DSM generation. It introduces a check and correction mechanism for stereo matching using the Euclidean distance as a benchmark. Then, an iterative strategy is proposed to correct the matching error, and it achieves superior accuracy in ground coordinates. A dataset consisting of GaoFen-3 SAR image triplets is used. The generated DSM is validated against open-source light detection and ranging (LiDAR) data. The experimental results show that the proposed method reduces the root mean square error (RMSE) by 24.1% compared with the traditional approach.

Change detection between images from different perspectives is a difficult problem in building change detection. At the same time, the quality of digital surface model (DSM) is very important for building change detection. We propose a new building change detection algorithm based on cross-temporal stereo matching to process images with different viewing angles using two temporal optical spaceborne stereo images. The algorithm matches two images with different temporal points and different viewing angles pixel by pixel to obtain unchanged regions and their elevations. Since the elevation of the unchanged area should be unchanged, we use the elevation obtained by cross-temporal stereo matching to correct the existing two temporal DSM. The proposed algorithm can correct the wrong elevation of the unchanged area, recover the matching failure area and fill the occluded area to obtain a more complete and accurate DSM. Applying the refined DSM and unchanged area mask to three existing building change detection algorithms, the performance of all algorithms has been greatly improved. The false alarm rate of the result is greatly reduced while the detection rate of changed buildings keeps almost unchanged.

The model-based inversion of spaceborne polarimetric interferometric synthetic aperture radar (PolInSAR) has great potential for large-scale forest height estimation. The inversion performance strongly depends on the accurate estimation of ground phase and volume coherence, and these aspects still need to be addressed due to the double-candidate effect of ground phase. This paper introduces a maximum a posteriori inversion method based on the two-layer randomly oriented volume over ground model to achieve a more accurate forest height estimation. Firstly, the components of the interferometric phase from different polarizations are analyzed and formulated. Among them, the topographic phase is found to be equivalent to the ground phase when disregarding the vertical error of the external DEM. Then, the ground phase is modeled as a Gaussian distribution based on the topographic phase and the external DEM. The maximum a posteriori estimate of the ground phase is derived by combining the prior distribution of the ground phase and the complex Wishart distribution of the observed covariance matrix. Finally, the forest height is inverted according to the optimally recovered volume-only coherence. The proposed method is verified through spaceborne L-band repeat-pass SAOCOM acquisitions over the forest test site in Foresta di Acquafrida, Santa Giusta, Province of Oristano, Italy. The inversion performance is validated against LiDAR data obtained from spaceborne ICESat-2 acquisitions. The results demonstrate that the proposed method reaches a mean error of 5.73 m and an RMSE of 8.04 m to the pixel level, which improves 36.02% and 24.05% compared with the existing method, respectively.

In this letter, a new technique based on genetic algorithm and support vector machine (GA-SVM) is proposed to effectively estimate the 3-D deformations of the earth’s surface by integrating sparse global navigation satellite system (GNSS) deformation measurements and interferometric synthetic aperture radar (InSAR) maps. The genetic algorithm (GA) is used to search the optimal supported vector machine (SVM) control parameters, considering the control parameters have an important influence on the prediction. Based on advanced machine learning theory, the proposed method has at least two main advantages over traditional methods: 1) it does not need to preinterpolate the displacements of GNSS points, and 2) it does not need to estimate the variance components of GNSS and InSAR point by point. Both the simulated and real experiments are implemented to prove the effectiveness of GA-SVM. In the real case of the Los Angeles, the root mean square errors of GA-SVM at 14 checkpoints are 7.92, 2.05, and 5.43 mm/a in the east–west, north–south, and vertical directions, respectively.

Due to the complex marine environment and high-order frequency modulation (FM) on radar echo from the micromotion of the target, the effective and robust detection of a marine target with micromotion under heavy sea clutters’ background is a challenging task. In this article, we propose a novel detection and estimation algorithm based on adaptive sparse modified-Lv’s transform (ASMLVT). First, the micro-Doppler (m-D) characteristics of marine targets are employed and modeled as quadratic frequency-modulated (QFM) signals. Second, we modify the 2-D robust sparse Fourier transform (2-D-RSFT) and make it adaptive to the sea clutters’ background, namely, 2-D adaptive sparse Fourier transform (2-D-ASFT). Then, we substitute the 2-D Fast Fourier transform (2-D-FFT) operation with 2-D-ASFT in the modified-Lv’s transform (MLVT). The proposed algorithm can not only achieve good energy accumulation and accurate parametric estimation for marine targets with micromotion but is also robust to the heavy sea clutters and can greatly reduce false alarms. Besides, it has a good cross-term suppression ability to detect multitargets. Experiments with simulated and real radar datasets show that the proposed algorithm can effectively detect and estimate multitargets with micromotion under heavy sea clutter and low signal-to-clutter ratio (SCR) background.

Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for few changed areas for traditional change detection methods. In this paper, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called Histogram Fitting Error Minimization (HFEM) is proposed for few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued difference image follows the Half-Normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on Half-Normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called Half-Normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only few changed datasets but datasets with lots of changes are used in the experiments. The Kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of few changed areas. Meanwhile, the proposed method can get similar results compared to traditional methods under normal conditions.

Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields. An automatic framework is provided that accurately recognizes the structure, type, and size of the room, and outputs vectorized 3D reconstruction results. Deep segmentation and detection neural networks are utilized to extract room structural information. Key points detection network and cluster analysis are utilized to calculate scales of rooms. The vectorization of room information is processed through an iterative optimization-based method. The system significantly increases accuracy and generalization ability, compared with existing methods. It outperforms other systems in floor plan segmentation and vectorization process, especially inclined wall detection.

The slant range errors caused by traditional hyperbolic range equation (THRE) with stop-and-go assumption will lead to image defocusing in high resolution spaceborne sliding-spotlight Bistatic SAR system (ST-BiSAR). In this paper, an accurate bistatic slant range model based on uniform acceleration curve motion (UARM) is proposed, which is precisely fitted with the actual range history. Then, a two-step imaging algorithm based on UARM and method of reversion series (MSR) is introduced to eliminate aliasing phenomenon and realize focus. Finally, simulation results verify the correctness and effectives of the proposed range model and imaging algorithms.

This letter presents the first differential synthetic aperture radar (SAR) tomography (D-TomoSAR) results using ground-based SAR (GB-SAR) data sets. GB-SAR provides an important deformation monitoring technology for glacier movements, landslides, and infrastructures because of its real-time monitoring capability compared with the airborne and spaceborne SAR sensors. A D-TomoSAR processing framework using region growing is proposed, which does not require the preliminary removal of atmospheric phase screen. The most reliable single-scatterers are identified as seeds, whereas the double-scatterers and unstable single-scatterers are resolved iteratively using region growing. First experimental results on 89 GB-SAR images over the Aletsch glacier, Switzerland, demonstrate the effectiveness of GB D-TomoSAR and the proposed method.

Interferogram denoising is a critical step in the interferometric synthetic aperture radar (InSAR) processing, which aims to filter out the noise as much as possible and simultaneously keep the edges of the interferometric fringes. In this paper, the relationship among the residues, the fringe rate, and the noise level is investigated to divide the pixels into three categories, i.e., scatters in high fringe rate area, low fringe frequency area and region contaminated by very serious noise. Each class of pixels are subsequently filtered by making different modifications to the classical Goldstein filter. The patch size is varying in different classes. Moreover, the density of the residues is introduced into the filtering parameter α. Besides, the smoothing function is also substituted in case of pixels in high fringe rate area. Finally, the proposed algorithm is validated by both the simulated experiment and real data test.

The sparse 3-D phase unwrapping (PU) is a significant problem in the time-series interferometric synthetic aperture radar (TSInSAR), which is a popular technique on the extraction of the ground deformation. The popular 1-D + 2-D strategies in 3-D PU provide an initial temporal estimation of the phase gradients followed by a spatial integration procedure. However, the temporal PU (TPU) solution is prone to be spatially inconsistent, which increases the difficulty of the subsequent spatial PU (SPU). To solve the problem, in this article, an improved global TPU formulation targeting at a totally irrotational phase-gradient field is first proposed, which, however, has a heavy computation complexity, because numerous variables have to be optimized simultaneously. Consequently, a more feasible and practical triangle-oriented TPU (TOTPU) strategy is subsequently suggested, in which a triangle rather than an arc is the elementary unwrapping unit with the spatial irrotational constraints imposed. Furthermore, a residue-minimization algorithm is proposed to settle the problem of the “conflict edges” introduced by TOTPU, which occurs in just a small portion of arcs. Finally, the improved TPU phase gradients are integrated using an L1-norm SPU method with a novel weight function to obtain more reliable unwrapped phases. The proposed triangle-oriented spatial-temporal (TOST) algorithm is validated to be effective and reliable with both the simulated data and the real TerraSAR-X images. Moreover, it can be transplanted into a parallel platform for large-scale applications.

Persistent scatterer interferometry (PSI) techniques exploit irregularly spaced permanent scatters (PSs) to extract the ground deformation. Sparse 2-D phase unwrapping is a significant procedure in PSI methods to reconstruct the phase function defined on a sparse data set given its value modulo 2λ . This letter first analyzes the phase unwrapping error of the residues and cuts related local methods when the cuts separate the sparse grids into several isolated regions. Then the space distribution of the cuts is converted into constraints on optimizing the Delaunay triangulation network. Finally, the phase jumps introduced by PSs with low quality are removed due to the more reasonable flows obtained by the constrained L1-norm method. Two experiments performed on the real data sets are presented to show the effectiveness and robustness of our algorithm, especially in the long-span cable-stayed bridge applications.

Repeat-pass spaceborne differential synthetic aperture radar interferometry (DInSAR) is commonly used to measure surface deformation. However, the phase delay due to the atmospheric water vapor has a significant influence on the accuracy of DInSAR results. On the other hand, the signal delay in DInSAR can give the spatial variation information of water vapor during the two image acquisitions. There are some ripple-like phase signals in DInSAR, which are probably due to the mountain wave in the study area. In this paper, the weather research and forecasting (WRF) model is used to simulate the atmospheric delay and compared to the phase delay derived from DInSAR results to understand the mountain-wave phases in interferograms. The results indicate that the ripple-like phase signal in the DInSAR phase is due to different intensities of the mountain wave in two acquisitions. The WRF model can be used to explain the mechanism of mountain-wave phase in DInSAR, which may then, hopefully, be used for DInSAR atmospheric correction.

Interferometric synthetic aperture radar (InSAR) image coregistration is a nontrivial task for its skew and distorted image pair, especially in severe decorrelated areas. In this letter, a new InSAR coregistration method, which considers both the coherence of the reference points and their topographical distribution, is proposed to perform accurate coregistration. First, a conventional cross-correlation registration method is performed, and a number of high coherent points are extracted as the reference points. Then, some well-distributed reference points are selected by utilizing the Voronoi diagram-based distribution optimization algorithm. Their subpixel correspondences are selected by finding the maximum values of the cross-correlation functions. Some singular correspondences are rejected by the random sample consensus method. Based on these accurate correspondences, the parameters of polynomial mapping function are estimated via the weighted least squares method. It improves the accuracy of geometrical mapping functions and overcomes the limitation of the conventional registration method when the reference points locate in low coherent area. C-band airborne repeat-pass acquired SAR data with 0.5-m resolution are used to validate the proposed algorithm by comparing with the conventional registration algorithm in accuracy. Experimental results prove the effectiveness of the proposed algorithm.

In this paper, an interferometric synthetic aperture radar phase denoising method which utilizes both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks, has been proposed. The derived nonlocal wavelet shrinkage use double L1 norm restrictions, which enforce local and nonlocal sparsity constraints by efficient shrinkage operators. This method can take advantage of the coefficients of nonlocal similarity between group blocks for wavelet shrinkage, and improve the accuracy of filtering result. Experimental results in InSAR phase image denoising tasks with simulation and actual noise data show that the proposed method outperforms the state of the art with lower root-mean-square error and less noisy fringes, making it possible to effectively filtering phase noise with superior performance.

InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. However, the important structure information is hardly quantifiable. This paper presents a novel index to evaluate the quality of InSAR interferogram based on connected area. After discomposing the fringes into independent connected areas, we analyze the statistical ratio of an area to its margin. Then we use the ratio as an index to quantitatively evaluate the interferogram. In the end, the presented index is used for the filtered interferogram of popular filters, and the results fit the visual judging.

Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.

The first-generation time-series synthetic aperture radar interferometry (TSInSAR) technique persistent-scatterer (PS) InSAR has been proven effective in ground deformation measurement over areas with high reflectivity by taking advantage of coregistered temporally coherent pointwise scatterers. In order to increase the spatial density of measurement points and quality of displacement time series over moderate reflectivity scenes, a second-generation TSInSAR called SqueeSAR was developed to extract displacement information from both PSs and distributed scatterers, by taking into account their temporal coherence and their spatial statistical behavior. In this paper, we propose a new second-generation TSInSAR, which is referred to as joint-scatterer (JS) InSAR, to measure the line-of-sight surface displacement using the neighboring pixel stacks. A novel goodness-of-fit testing approach is proposed to analyze the similarity between two JS vectors based on time-series likelihood ratios. By taking advantage of the proposed test, a new spatially adaptive filter is developed to estimate the covariance matrix. Based on the estimated covariance matrix, the projection of the joint signal subspace onto the corresponding joint noise subspace is applied to retrieve phase history. With coherence information of neighboring pixel stacks, JSInSAR is able to provide reliable geophysical parameters in the presence of large coregistration errors. The effectiveness of the proposed technique is verified with a time series of high-resolution SAR data from the TerraSAR-X satellite.

The “stop-go” approximation is widely used for the processing of synthetic aperture radar (SAR) data, and the error brought by this assumption can be negligible for most SAR systems. However, for the SAR on a high-speed platform, with the increasing requirements on high-resolution imaging, the error may be intolerable for SAR imaging. In this case, the radar motion within a pulse repetition interval should be taken into account for the echo model and imaging algorithm. In this paper, according to the geometric configuration of the SAR working process, an accurate echo model is presented. By comparing the “stop-go” echo (which denotes the echo based on the “stop-go” approximation in this paper) with the accurate echo, the error brought by the “stop-go” approximation is introduced, and the intolerable error is shown in a reference system. A spotlight imaging algorithm based on the accurate echo is given and is well supported by the simulation results.

A new inverse synthetic aperture radar (ISAR) imaging approach is presented for application in situations where the maneuverability of noncooperative target is not too severe and the Doppler variation of subechoes from scatterers can be approximated as a first-order polynomial. The proposed algorithm is referred to as the range centroid Doppler (RCD) ISAR imaging technique and is based on the stretch Keystone-Wigner transform (SKWT). The SKWT introduces a stretch weight factor containing a range of chirp rate into the autocorrelation function of each cross-range profile and uses a 1-D interpolation of the phase history which we call stretch keystone formatting. The processing simultaneously eliminates the effects of linear frequency migration for all signal components regardless of their unknown chirp rate in time-frequency plane, but not for the noise or for the cross terms. By utilizing this novel technique, clear ISAR imaging can be achieved for maneuvering targets without an exhaustive search procedure for the motion parameters. Performance comparison is carried out to evaluate the improvement of the RCD technique versus other methods such as the conventional range Doppler (RD) technique, the range instantaneous Doppler (RID) technique, and adaptive joint time-frequency (AJTF) technique. Examples provided demonstrate the effectiveness of the RCD technique with both simulated and experimental ISAR data.

Ground moving target indication (GMTI) is one of the most important applications in a general bistatic synthetic aperture radar (SAR) system, where the transmitter and receiver move along nonparallel trajectories with different velocities. In order to improve the capability of clutter cancellation in bistatic SAR/GMTI processing, the coherence between two echoes collected by two receivers is investigated, and the full-coherence conditions are derived. A new coherence-improving algorithm for general bistatic SAR complex image pairs is proposed, which can be realized in the following steps: 2-D range azimuth prefiltering processing, relative geometric deformation correction, and image registration. An approximate implementation of 2-D prefiltering and the corresponding prefilter parameter analysis are also given. Last, two numerical experiment results are given to demonstrate the effectiveness of the proposed algorithm.