Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period.

Light detection and ranging (LiDAR)-derived digital surface models (DSMs) provide high-resolution representations of the Earth’s surface and serve as important input data for 3D urban modeling. However, these datasets are inevitably noisy due to sensor ranging errors and the inherent roughness of surface materials. In dense urban areas, traditional spatial-domain filters rely on a local smoothness assumption and therefore struggle to handle abrupt height variations, such as building edges, resulting in signal-to-noise aliasing. To address this issue, we propose a novel phase-domain post-filter approach (PFA) that transfers the denoising process from the spatial domain to the phase- domain by introducing a virtual period parameter. LiDAR elevation data are first mapped into wrapped phase fringes, after which a frequency-domain Goldstein filter is applied to effectively separate noise from structural information according to their spectral characteristics. Finally, the denoised elevation model is reconstructed through constrained phase unwrapping. Experimental results on dense urban LiDAR datasets from New York City and Dublin demonstrate that, compared with conventional filters, the proposed method better preserves the vertical edges of buildings while effectively smoothing flat regions.

Accurate identification of land subsidence types is essential for effective urban risk management, yet remains challenging due to the superposition of deformation signals at different spatial scales and the complexity of urban environments. In this study, land subsidence types in Fuzhou were identified through an integrated framework combining multi-scale deformation analysis and ensemble learning. Ground deformation time-series measurements were derived from 66 Sentinel-1A synthetic aperture radar (SAR) observations acquired between January 2018 and June 2023 using the time-series interferometric synthetic aperture radar (TS-InSAR). Deformation values in decorrelated areas were subsequently reconstructed using regression models driven by multi-source geological, hydrological, land-use, and urban features, resulting in a spatially continuous deformation field. A Fast Fourier Transform (FFT)-based Butterworth filtering approach was then applied to separate regional-scale and local-scale subsidence signals. Based on the extracted local deformation patterns and discriminative auxiliary features, land subsidence was classified into five categories: farmland-related subsidence, linear infrastructure-related subsidence, low-lying stratum-related subsidence, land-use transition-related subsidence, and older building area-related subsidence. Three ensemble learning models, XGBoost, CatBoost, and LightGBM, were implemented for subsidence type classification. All models achieved satisfactory performance, among which LightGBM exhibited the best overall performance. The classification results reveal pronounced differences in spatial distribution and deformation intensity among subsidence types. Farmland-related subsidence occupies the largest proportion of the affected area but is characterized by relatively moderate deformation rates, whereas older building area-related subsidence, despite its limited spatial extent, exhibits the highest deformation intensity. This study demonstrates the potential of ensemble learning for land subsidence type classification.

Landslide mapping from InSAR-derived active deformation areas (ADAs) is challenging in mountainous regions because InSAR results often contain residual processing artifacts, uneven measurement-point distributions, and real but non-target surface deformation. Conventional statistical-threshold and spatial-statistical approaches can identify anomalous and clustered deformation, but such clusters are not necessarily process-compatible with landslide-related deformation. This study proposes a mechanism-informed hierarchical framework to make ADA object realization more process-compatible and auditable under weak-ground-truth conditions. Rather than treating ADA extraction as one-step thresholding and clustering, the framework embeds object-scale priors, local deformation-field regularity, and geomorphic feasible domains into different stages of the interpretation chain. The method was tested in Dashu Town, Fengjie County, in the Chongqing section of the Three Gorges Reservoir Region, and was compared with statistical-threshold and spatial-statistical baseline methods. Results show that the proposed framework can provide interpretable conservative, balanced, and recall-oriented ADA candidates. Area-class and cross-entropy analyses indicate that the conservative and balanced routes show stronger object-size consistency with mapped unstable slopes than the baselines. Local UAV and field observations further suggest that the framework provides more spatially refined segmentation of adjacent deformation objects. Parameter sensitivity analysis shows that the key segmentation parameters have interpretable effects on ADA polygon realization. The proposed framework offers an auditable and engineering-feasible post-processing strategy for regional InSAR-based landslide mapping.

As a classic data processing tool, Principal Component Analysis (PCA) has been widely applied in various data analysis applications. To mitigate the high computational complexity of PCA on big data, distributed PCA methods have been extensively studied, which disperse the computational tasks across multiple computation units while guaranteeing the accuracy. For the scenarios of distributed PCA in wireless networks, as the data is originally dispersed across different locations, it is further required to reduce the communication cost of distributed PCA in networks, which however has been seldom studied. Reducing the communication cost of distributed PCA in wireless networks requires not only appropriately partitioning the computation of PCA, ensuring accuracy, but also effectively assigning the partitioned computations and routing strategies to the nodes. In this paper, we propose CD-PCA, a communication-efficient distributed PCA (CD-PCA) scheme. This scheme implements a transmission-benefit equipartition strategy for the network to facilitate high-accuracy distributed computation and designs novel routing strategies for nodes to execute the distributed PCA within each partitioned region. Extensive simulation results demonstrate that the proposed CD-PCA scheme can reduce transmission costs by over 30% on average compared to related methods and baseline approaches.

To improve the quality of products obtained by interferometric phase signal time-series analysis of synthetic aperture radar (TS-InSAR) techniques, various phase reconstruction approaches have been developed recently. The commonly used methods assume that synthetic aperture radar (SAR) observations are complex circular Gaussian (CCG) distributed, making them vulnerable to outliers. Noting this, a robust probabilistic generative SAR (RPGSAR) interferometric phase reconstruction method, named RPGSAR, is proposed in this article. The RPGSAR introduces a multivariate complex t-distribution (MCT) to a probabilistic generative model, which establishes a projection matrix depicting statistical behaviors of reconstructed phase. Furthermore, to resolve this model, a targeted expectation maximization (EM) algorithm is deduced and implemented. It is worth noting that the degree of freedom (DOF) can be automatically estimated by the RPGSAR. To validate the RPGSAR, we first apply the phase linking (PL) and principal components analysis (PCA) methods to simulated data. The experimental results demonstrated the RPGSAR's ability to handle outliers. Furthermore, real Sentinel-1A SAR stacks consisting of different number of images are reconstructed by these methods. The results show that the proposed method can provide more consistent interferometric phases in all stack size cases compared with the PL and PCA methods, thus further confirming its effectiveness.

Accurately locating and studying grounding lines is essential for predicting the response of glaciers to climate change. However, it is challenging to find grounding lines since they are subglacial features. In this study, Sentinel-1 synthetic aperture radar (SAR) data were utilized to derive the grounding lines of the Riiser-Larsen Ice Shelf. A new method with inspiration drawn from multi-temporal baseline InSAR techniques is proposed. It takes advantage of the temporal consistency of the vertical displacement gradients and identifies grounding zones pixel-by-pixel on a stack of double differential interferograms, thereby providing grounding line proxies. As it fully exploits coherent signals in both spatial and temporal domains, the maximum possible number of grounding zone pixels can be obtained. Moreover, due to the introduction of the concept of the temporal consistency, the method can cope with short term grounding line fluctuations to some extent and may mitigate the influences of atmospheric disturbances and residual ice displacements. The resulting grounding lines are compared with the MEaSUREs Antarctic grounding line product. The comparison confirms the effectiveness of the proposed method and corroborates that the Riiser-Larsen Ice Shelf should have not undergone significant changes over the past few decades.

Low-rank models have been widely applied for visual analysis. However, the conventional global low rank on a single whole image and the patch-level low rank have difficulty in perfectly preserving dependence (or correlation) and the latent structures in the image. Inspired by recent advances in low-rank tensor analysis, a wavelet-based low rank tensor regularization model (WLTR) is proposed in this work. Utilizing the fact that the coefficients of an image under wavelet transform exhibit sparsity, the low-rank regularization can be obtained by imposing the nuclear norm constraint on the tensor composed of wavelet subbands coefficients. Considering all structure information of intrascale and interscale coefficients under the tensor representation, the proposed method characterizes the local and global elements in a unified manner. To make the proposed WLTR tractable and robust, the alternative direction of multiplier method (ADMM) is adopted to efficiently and effectively solve this convex optimization problem. Extensive experiments on various types of image restoration problems, including inpainting, deblurring and denoising, validate WLTR outperforms several existing state-of-the-art approaches in terms of peak signal-to-noise ratio and visual quality.

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring (SHM), volcano tomography, and machine diagnosis, all the raw data in WSNs are required to be gathered to the sink where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large-scale raw data transmission while guaranteeing the global feature quality, there are two kinds of solutions: one is in-network processing, which generally needs to distribute the centralized computation of feature extraction into networks. Another solution is compressive sensing (CS) followed with the feature extraction (called feature CS in this article). An interesting question is: for in-network processing and feature CS, which kind of solutions is more cost efficient to accomplish the task of feature extraction? This question is seldom studied. To answer it, we take the case of SHM with WSNs along with the classic feature extraction algorithm, i.e., the Eigen-system realization algorithm (ERA), and appropriately design two novel routes for in-network processing and feature CS, respectively. Both theoretical analysis of the two solutions’transmission cost and numerous simulations have been conducted. Based on the comparison results, we summarize some guidelines on the solution choice for different kinds of WSNs for SHM. In addition, we find that, instead of guaranteeing the quality of raw data reconstructed, CS with guaranteeing the quality of feature extracted is usually more meaningful and cost efficient.

Energy-efficient data gathering in Wireless Sensor Networks (WSNs) has been widely studied, and compressive sensing (CS) is an effective solution. CS can guarantee the sink node to accurately obtain all sensor nodes’ raw data with low data gathering cost, which is unlike the data aggregation solutions that usually result in large information loss. The existing compressive sensing methods used in WSN, either all nodes participate in compressive sensing computation which means all nodes need to delivery cumulative projection results with a size several times of raw data, or assume that the communication capability of nodes is heterogeneous (some nodes have the communication capability of one hop to sink). This paper aims at general WSN consisting of sensor nodes with isomorphic communication capability, effectively designs a new hybrid routing algorithm for two categories of nodes (i.e., perform compressive sensing calculation or not). This algorithm reduces part of sensor nodes’ transmission cost when applying compressive sensing, while remaining the quality of reconstructed data. Extensive number of simulation experiments have been done, and the results verify the advantages of this method over the existing methods.

In recent years, MT-InSAR techniques have gained widespread use in the field of remote sensing. Among these techniques, PS-InSAR is a prominent method. To improve the density and reliability of PS-InSAR products, a two-stage SAR signal phase reconstruction scheme, referred to as TSSPR, is proposed in this paper. The effectiveness of this approach was evaluated by applying it to PALSAR images obtained from February 11, 2007 to February 22, 2011, over Mexico City. The experimental results demonstrate that TSSPR significantly enhances the number and reliability of targets in MT-InSAR applications.

In this work, noise enhanced parameter estimation problems are investigated for a general nonlinear system, where an additive noise is added to the nonlinear system input and a Bayesian estimator is developed based on the noise modified output. The optimal probability distribution of the additive noises is formulated successively for minimizing the mean square error (MSE) of the optimal Bayesian estimation and the Cramer–Rao lower bound (CRLB). Then the optimal additive noises for the two different noise enhanced optimization problems are explicitly derived as constant vectors, which implies the randomization of constant vectors is not beneficial to these optimizations. Finally, numerical results are presented to illustrate the theoretical results.

Compressive Sensing has been validated as an efficient way for data gathering in chain-type wireless sensor networks (WSNs). Given the sparsity of the sensor nodes’ raw data, each sensor node just needs to send a constant size of data during the multi-hop data gathering, instead of relaying increasing size of data as in traditional data gathering works. However, we notice that the sparsity of raw data in event-involved local region may not be the same as that in the other usual region, which gives us hint that existing compressive sensing based on uniform sparsity of data actually can be improved. In this paper, we propose an adaptive compressive data gathering method A-CS for chain-type wireless sensor networks. Meanwhile, we design an ultra-lightweight localized sparsity variation detection mechanism for determining the event-involved region. Numerous simulation has been conducted, showing the efficiency of the proposed A-CS method.

On 21 October 2017, days of heavy rainfall triggered a landslide in Guang’an Village, Wuxi County, Chongqing, China. According to the field investigation after the incident, there is still a massive accumulation body, which could possibly reactivate the landslide. In this study, to explore the long-term evolution of the deformation after the initial Guang’an Village Landslide, a time-series InSAR technique (TS-InSAR) was applied to the 128 ascending Sentinel-1A datasets spanning from October 2017 to March 2022. A new approach is proposed to enhance the conventional TS-InSAR method by integrating LiDAR data into the TS-InSAR process chain. The spatial–temporal evolution of post-event deformation over the Guang’an Village Landslide is analyzed based on the time-series results. It is found that the post-event deformation can be divided into three main stages: the post-failure stage, the post-failure and reactivation stage, and the reactivation stage. It is also suggested that, although the study area is currently under the reactivation stage, there are two active deformation zones that may become the origin of a secondary landslide triggered by heavy rainfall in the future. Moreover, the nearby Yaodunzi landslide might also play an important role in the generation and reactivation of a secondary Guang’an Village Landslide. Therefore, continuous monitoring for post-event deformation of the Guang’an Village Landslide is important for early warning of a secondary landslide in the near future.

2D magnetic resonance spectroscopy (MRS) is extensively used to analyze the components, structures, and interactions of substances in chemistry and bioengineering. To reduce data acquisition time, the spatiotemporally encoded ultrafast (STEU) MRS employs a fast means to acquire data in the hybrid time and frequency (HTF) plane, but relies on non-uniform sampling (NUS) technique. After that, a proper reconstruction method is essential to recover a high quality 2D MRS from undersampled HTF data. In this work, each column and row of 2D MRS are converted into Hankel matrices, by which a simultaneous low rank regularization model is proposed to exploit the bivariate exponential structure of 2D MRS signal. Additionally, a non-convex surrogate function for rank is integrated in the proposed model to more precisely enforce the low rank property of Hankel matrices. To ensure computational accuracy and convergence, an efficient numerical algorithm is further deduced based on alternating direction method of multipliers (ADMM) iteration. The experimental results have shown that the proposed method significantly outperforms existing 2D MRS reconstruction methods, and presents a strong robustness to low sampling rates, various sampling patterns, and measurement noise with a favorable complexity.

Sparse representation of image is crucial to a promising reconstruction in compressed sensing magnetic resonance imaging (CS-MRI), which significantly accelerates imaging process by highly undersampling k-space data. However, realizing high sparsity and estimation accuracy of image coefficients directly affects the visual quality of the reconstructed image. In this work, an adaptive 3D transform learning method is developed to efficiently enhance the sparsity of coefficients produced by 3D transform on similar patches. In addition, we also consider the statistical characteristics of 3D coefficients, and build a probabilistic model that employs Gaussian scale mixture (GSM) prior for the grouped 3D coefficients with close magnitude levels. Based on Bayesian inference, a piecewise sparsity constraint is derived from maximum a posterior (MAP) estimation of 3D coefficients. Furthermore, the combination of piecewise sparsity constraint and adaptive 3D transform allows one to establish a novel CS-MRI reconstruction model and the corresponding numerical algorithms are deduced under the framework of alternating direction method of multipliers (ADMM). Compared to several advanced CS-MRI methods, the proposed approach better suppresses artifacts and preserves more image features with superior performance metrics.

Compressed sensing (CS) allows accelerated magnetic resonance imaging (MRI) by highly undersampling k-space data. The key to high quality CS-MRI reconstruction is rational utilization of the sparsity of image in a certain transform domain. Existing CS-MRI methods commonly uses l0 norm or l1 norm to enforce the sparsity of image coefficients but lack parameter adaptation. In this work, a patch level sparse representation is derived from the joint maximum a posteriori (MAP) estimation under a probabilistic model, which adopts a hierarchical prior to characterize sparse image coefficients. The corresponding image reconstruction model is efficiently optimized by alternating direction method of multipliers (ADMM). Simulation results reveal that the proposed approach achieves higher reconstruction performance than competing CS-MRI methods, and is proven to be superior to general Ip norm based methods.

Compressed sensing (CS) theory speeds up the magnetic resonance imaging (MRI) by undersampling the k-space data. Utilizing a proper sparse representation for image and incorporating prior information are vital to yield a high-quality reconstruction in CS-MRI. In this work, the multivariate Gaussian scale mixture (GSM) model is developed to precisely characterize to the statistical properties of sparse coefficients of group formed by similar patches, and a Bayesian group sparse representation (BGSR) is derived from maximum a posterior (MAP) estimation. The efficient multiclass orthogonal dictionaries learning is further integrated in BGSR driven CS-MRI reconstruction to enable high sparsity as well as performance enhancement. The solution is obtained by a use of alternating direction method of multipliers (ADMM) iteration, and a closed-form solution for each subproblem is separately deduced. The experimental results show that the proposed method provides a superior visual quality and performance indexes over the regularization based CS-MRI methods. Hence, the proposed method can be utilized to further accelerate MRI and produce the highly accurate reconstruction for subsequent image processing as well as clinical diagnosis. Furthermore, this work can be extended to other imaging applications and provide some references for Bayesian probabilistic model based CS-MRI reconstruction. (c) 2021 Elsevier B.V. All rights reserved.

Multi-baseline interferometric synthetic aperture radar (InSAR) techniques have been accepted as effective remote sensing tools for detecting and monitoring landslide movements. With the use of stacked synthetic aperture radar (SAR) imageries, it is capable of generating precise ground displacement time-series. In order to further suppress noise induced by atmospheric effects, a post-process step, named as temporal filter, is required to be applied to the final displacement time-series in most applications. As displacement signals are strongly correlated in time, the traditional window-based/least squares filter is widely adopted. Since the window-based filter balances a tradeoff between noise smoothing and signal smoothing, the resulting time-series may strongly deviate from the true values when ground displacements appear high nonlinearity. In this paper, a new approach is proposed to reconstruct the InSAR deformation time-series for rainfall-induced landslides. This method establishes a nonparametric model based on the idea of Gaussian process regression (GPR) and introduces precipitation data as a priori knowledge. A strong relationship between rainfall history and ground movements is therefore constructed, which is extremely helpful in preventing the loss of high-frequency displacement signals. The proposed approach was applied to the InSAR landslide displacement time-series obtained from 108 European Space Agency (ESA) Sentinel-1A satellite SAR images. Experimental results demonstrate that it is capable of preserving the details of the temporal evolution of ground displacements effectively compared to the traditional window-based method, in particular on the surface of sliding mass.

In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.