Acknowledging different wavelengths by imaging mechanisms, optical images usually embed higher low-dimensional manifolds into ambient spaces than SAR images do. How to utilize their complementarity remains challenging for multimodal clustering. In this study, we devise a conditional dual diffusion (CDD) model for multimodal clustering of optical and SAR images, and theoretically prove that it is equivalent to a probability flow ordinary differential equation (ODE) having a unique solution. Different from vanilla diffusion models, the CDD model is equipped with a decoupling autoencoder to predict noises and clear images simultaneously, preserving data manifolds embedded in latent space. To the fuse manifolds of optical and SAR images, we train the model to generate optical images conditioned by SAR images, mapping them into a unified latent space. The learned features extracted from the model are fed to K-means algorithm to produce resulting clusters. To the best of our knowledge, this study could be one of the first diffusion models for multimodal clustering. Extensive comparison experiments on three large-scale optical-SAR pair datasets show the superiority of our method over state-of-the-art (SOTA) methods overall in terms of clustering performance and time consumption. The source code is available athttps://github.com/suldier/CDD.

Extracting training datasets for supervised classification of Synthetic Aperture Radar (SAR) images is complicated, due to e.g. poor radiometric resolution, speckle noise, and lack of reference data. It is challenging to link radar scatterers in SAR images with the counterparts in the reference datasets registered in geographic coordinate systems. To address this issue, this paper proposes a method called Rdr-Code to radarcode geodetic reference datasets for creating SAR training datasets for machine learning applications. To assess the importance of building heights in radarcoding, we compared the assignment of height values by a minuscule pseudo height with the actual building heights derived from a Lidar-based DEM product. We used 30 PAZ SAR images in X-band, which were acquired between 2019 and 2021, over the north-west part of the Netherlands, and employed Top10NL and AHN as reference LULC polygon and height datasets respectively. The radarcoding accuracy was compared using nine buildings as references in the SAR coordinates. The radarcoding accuracy was 64.5% with the pseudo height and 84.5% with actual building heights. A trade-off between accurate building feature information and separation between close buildings was observed. We conclude that this is an effective way to radarcode reference datasets and can be used for crafting training datasets for machine learning methods.

Satellite images of synthetic aperture radar (SAR) sensors are contaminated by speckles from the coherent imaging mechanism. Although removing or mitigating speckle has been a critical issue for SAR applications, effective reduction continues to be a significant challenge for existing methods when preserving the intricate structures within SAR images. To address this issue, this work proposes a novel conditional diffusion model for SAR despeckling (DiffusionSAR). The new method explicitly learns data distributions by forward diffusion toward multiplicative gamma noise. The logarithmic and Yeo–Johnson (log-Yeo–Johnson) transformation are harnessed in preprocessing for fine-tuning or hybrid training. A prolonging steps technique is suggested in fine-tuning to match the preprocessing. A new synthetic dataset is designed for satellite SAR despeckling. The proposed method is compared with eight state-of-the-art methods using both synthetic and real-world SAR satellite images. The qualitative and quantitative evaluations confirm the effectiveness of the proposed method in structural preservation as well as noise reduction. A fine-tuning experiment using stacked multitemporal data shows the necessity of tine-tuning training in bridging the domain gap when trained with synthetic data and tested with real-world SAR data.

This study investigates the PAZ SAR data in both VV and HH channels, contributing to PAZ product assessment. It focuses on deformation maps and deformation time series over a test site in Leeuwarden, the Netherlands. To optimize the estimates of the deformation time series and parameters of the temporal model, we developed and applied a model-backfeed method (MBF). This MBF iteratively re-introduces into phase unwrapping the best deformation model of every Constantly Coherent Scatterer, such as Persistent Scatterer (PS), determined by Multiple Hypothesis Testing (MHT). In this regard, distinct temporal behavior of individual scatterers is considered and modeled, thus improving the estimates of deformation time series. 24 co-polarimetric SAR data acquired between 2019 and 2021 are used for our test. The test result shows that PAZ SAR in HH offered 7% more PS than those in VV, and the VV mode identified a bit more PS along line-infrastructure like roads. Besides, by comparison and GNSS-based validation, we find that co-pol PAZ products are generally of a good quality. The MBF method increases the average ensemble coherence by 38% for VV and 35% for HH, and decreases the average spatio-temporal consistency by 2.3% for VV and HH and mitigates phase unwrapping errors, thereby optimize deformation estimates.

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

Synergizing SAR multi-sensors facilitates long-term deformation time series monitoring of radar scatterers on the Earth surface. Due to the disparity in e.g. radar wavelength, incidence angle, orbital direction, and polarization, however, there is no straightforward way to concatenate such time series from different SAR sensors. This study as an extension of [1] proposes the use of tie-point pairs, i.e. scatterers that are most likely reflected from a common ground target, aiming at integrating multi-sensor SAR data to monitor surface deformation without the loss of spatial resolution. Tie-point pairs are identified using geolocation uncertainty of radar scatterers. A probabilistic temporal model of tie-point pairs' time series is developed to link deformation time series from different sensors. We tested the proposed approaches in Groningen, The Netherlands, using 82 Radarsat-2 (C-band, July 2009 - June 2015) and 13 ALOS-2 (L-band, September 2014 - May 2020). Finally we identified 3315 tie-points with three different intersection types and determined their best temporal models. For those points, the maximum vertical subsidence velocity is up to 10 mm yr-1 between 2009 and 2020.

Satellite-based persistent scatterer satellite radar interferometry facilitates the monitoring of deformations of the earth's surface and objects on it. A challenge in data acquisition is the handling of large numbers of coherent radar scatterers. The behavior of each scatterer is time dependent and is influenced by changes in deformation and other phenomena. Built environments are especially challenging since scatterers may have different signal qualities and deformations may vary significantly among objects. Thus, the estimation of the actual deformation requires a functional model and a stochastic model, both of which are typically unknown per scatterer and observation. Here, we present an approach that models the deformation behavior for each individual scatterer. Our technique is applied in a postprocessing phase following the state-of-the-art interferometric processing of persistent scatterers. This addition significantly improves the interpretation of large data sets by separating the relevant phenomena classes more efficiently. It leverages more information than other methods from individual scatterers, which enhances the quality of the estimation and reduces residuals. Our evaluation shows that this technique can discriminate objects in terms of similar deformation characteristics that are independent of the specific spatial position and temporal complexity. Future applications analyzing large data sets collected by satellite radars will, therefore, drastically benefit from this new capability of extracting categorized types of time series behavior. This contribution will augment traditional spatial and temporal analysis and improve the quality of time-dependent deformation assessments.

Land reclamation is a pragmatic urban solution for coastal development, serving for building harbors, airports, industrial zones. Considering the intensive human activities over the reclaimed land and natural processes such as soil compaction and coastal erosion, the reclaimed lands may show graduate or instantaneous subsidences. It is of great significance to consecutively monitor and detect such subsidences before they unveil themselves as hazards. Here we use multi-epoch InSAR (Synthetic Aperture Radar) technique to produce deformation time series of the InSAR measurement points. To detect anomalies in the deformation time series of any individual ground object, we first use laser scanning data - AHN3, to adjust the geoposition of the InSAR measurement points and classify the objects e.g. ground, buildings, vegetation and water. Next we use the topographic map - TOP10NL, to recognize every individual object, e.g. a single building. As such, the precision of the geolocation of the InSAR measurement points can be improved, and the InSAR measurement points from any individual object, defined as a cluster (a group of spatial-related points), can be categorized among the others. Then we use a cluster-wise multiple hypothesis testing method to identify the spatial and temporal anomalies. Our methods are demonstrated in the case study of the Rotterdam reclaimed areas, The Netherlands.

The estimation of fresh snow depth (FSD) using X-band synthetic aperture radar (SAR) is feasible but challenging depending on the hydrometeorological conditions and data availability. In this study, the FSD is computed for the Beas river watershed in the northwestern Himalayas near Manali, India. It incorporates the recent copolar phase difference (CPD) based FSD inversion model. Moreover, the TerraSAR-X and TANDEM-X bistatic data acquired in January 2016 are used as inputs to the model along with the snow density measurements at the Dhundi ground station. Additionally, apart from applying layover and forest masks, the potential uncertainty sources in the complex mountainous terrains are identified using the H-A-α decomposition and unsupervised Wishart classification techniques. Furthermore, due to the limited number of weather stations, the results are validated using a 3×3 neighbourhood window surrounding the Dhundi site. Also, the effects of different FSD ensemble window sizes are tested for performing sensitivity analysis.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas.

Satellite radar interferometry (InSAR) has been used to monitor the structural health of line-infrastructure (e.g. railways, bridges, dams and dikes) in recent years. This enables the retrieval of millimeter-level changes in the line-infrastructure geometry on a bi-weekly basis. However, InSAR is an opportunistic method for which the location of the measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products vary from one case to another. Particularly, this is due to the orientation of the line-infrastructure relative to the satellite position, and its expected deformation magnitude and direction. Hence, the InSAR applicability and performance quality is not uniform. In operational situations, this tends to make asset managers skeptical about the potential of InSAR application on these assets. In this work, following [1] we develop new standard InSAR products for line-infrastructure monitoring, provide tools for predicting optimal multi-sensor SAR data combinations, and propose generic a priori performance assessment metrics for line-infrastructure. These products and metrics are tested on the Dutch railway line-infrastructure asset.

PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.

InSAR time series analysis involves the processing of extremely large datasets to estimate the relative movements of points on Earth. The estimated movements may reveal geophysical processes, or strain in anthropogenic structures. In parametric estimation methods, it is important to chose the optimal mathematical functional model relating the satellite observations to the kinematic parameters of interest. A standard approach is to parameterize the kinematic behavior, in first order, as a linear function of time, but it is unlikely that all objects behave in this purely linear way. Ideally, the kinematic parameterization should be optimized for each individual measurement point in the area of interest. In this work, following [1] we introduce a method to select the optimal functional model, with a minimum but sufficient number of free parameters using a probabilistic method based on multiple hypotheses testing.

Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellite missions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique.

Multitemporal interferometric synthetic aperture radar (InSAR) is increasingly being used for Earth observations. Inaccurate estimation of the covariance matrix is considered to be the most important source of error in such applications. Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing large time spacing due to the variability of the temporal sample. Moreover, these methods are computationally intensive. In this paper, a new algorithm named fast SHP selection (FaSHPS) is proposed to solve both problems. FaSHPS explores the confidence interval for each pixel by invoking the central limit theorem and then selects SHPs using this interval. Based on identified SHPs, two estimators with respect to the despeckling and the bias mitigation of the sample coherence are proposed to refine the elements of the InSAR covariance matrix. A series of qualitative and quantitative evaluations are presented to demonstrate the effectiveness of our method.

Conventional PSI technology is aimed towards estimating displacement time series of persistently coherent scatterers (PS) from a given set of radar acquisitions. Whenever the data from a new acquisition become available, the estimators for the parameters of interest will be computed by re-adjustment of the system of equations. This strategy of batch processing after a new acquisition is not optimal to identify changes in the behavior of single scatterer. For monitoring the structural health of buildings and civil infrastructure, there is a need for fast identification of anomalous behavior of scatterers, including the likelihood estimations of such detection results. Here we propose a general framework for the detection of anomalous behavior of (parts of) buildings and civil infrastructure by generating a sequential update of conventional interferograms, in combination with the parallel processing of the data using time series (PSI) interferometry. By estimating and analyzing the phase change per arc from each wrapped interferogram, abnormal changes can be detected fast and reliably. Our approach is demonstrated on a near-collapse of a building in Heerlen, the Netherlands, using Radarsat-2 data.