Coherence magnitude is a key metric for assessing the similarity between two synthetic aperture radar (SAR) signals, playing an essential role in high-quality interferometric SAR (InSAR) phase processing and the analysis of Earth’s surface characteristics. However, coherence estimation often suffers from upward bias due to limited independent samples, particularly in low-coherence regions. This study proposes a regularization-based approach to reduce coherence estimation bias, specifically in areas with limited homogeneous samples. By adaptively determining regularization parameters and prior coherence using nonlocal homogeneous pixel estimation, the method effectively minimizes bias while maintaining computational efficiency. Validated through Monte Carlo simulations and real data from 16 TerraSAR-X images of Shanghai, the proposed approach outperforms conventional techniques, exhibiting reduced residual bias and lower estimation standard deviation across diverse scenes.

Accurate sleep staging is crucial for the early diagnosis of neurodegenerative diseases and the management of sleep disorders. To provide a user-friendly, non-intrusive, and long-term monitoring solution, we explored the potential clinical applications of ear-electroencephalogram (ear-EEG). This study proposes a probabilistic ensemble learning approach for automatic sleep staging using single-channel ear-EEG data. The proposed method integrates Extreme Gradient Boosting (XGBoost) with Linear Discriminant Analysis (LDA), augmented by transition matrix correction and probability weighting strategies, to capture temporal sleep patterns without compromising data integrity or requiring intensive preprocessing. An ear-EEG with polysomnography (ear-PSG) dataset collected from twenty subjects using our custom-developed ear-EEG sensor, was compared with two public datasets, ear-Feature and Sleep-EDF, to validate both the reliability of the data and the effectiveness of the proposed approach. The results indicate that transition matrix correction is particularly effective when training and testing are conducted using single-epoch inputs, whereas model weighting demonstrates greater stability as the number of epochs increases. When using seven-epochs input sequences, leave-one-subject-out (LOSO) cross-validation achieved 0.814 accuracy with 0.749 kappa coefficient on ear-PSG (earL-R), and 0.841 accuracy with 0.779 kappa coefficient on the ear-Feature dataset. The design of a single-channel cross-ear intra-auricular ear-EEG configuration, combined with an ensemble learning framework, effectively balances device portability and classification performance, offering new insights for the clinical translation of wearable sleep monitoring technology and laying a foundation for the development of portable sleep monitoring devices.

Interferometric Synthetic Aperture Radar (InSAR) has become an essential tool for monitoring surface deformation with high precision and wide spatial coverage. Among various InSAR techniques, the Stacking InSAR approach is widely used for geological hazard assessments due to its computational efficiency and robustness against decorrelation noise. However, Digital Elevation Model (DEM) errors remain a significant challenge, introducing spurious deformation signals and degrading deformation rate estimates. We present here a rigorous analysis of the impact of DEM errors on Stacking InSAR-derived deformation rates and introduce an enhanced method that effectively mitigates these errors. Unlike conventional correction techniques that require explicit DEM error estimation, the proposed method leverages perpendicular baseline averaging, eliminating DEM-induced biases while maintaining computational simplicity. The method is validated using both simulated and real Sentinel-1A datasets from two tracks, with results compared against conventional Stacking and Small Baseline Subset (SBAS) approaches. The findings demonstrate that the proposed method significantly improves deformation rate estimation by suppressing DEM-induced artifacts, thereby enhancing the reliability of InSAR applications in geological hazard monitoring and deformation assessment.

In permafrost regions, ground surface deformations induced by freezing and thawing threaten the integrity of the built environment. Mapping the surface displacement of the ground at a high spatial resolution is of practical importance for the construction and planning sectors. In central Yakutia (the Sakha Republic), the long-term trend displays a consistent mean annual air temperature (MAAT) increase from -9.6 ‰ to -6.7 ‰, with pronounced temperature anomalies in the last decade. We processed Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data from 2017 to 2021, acquired over the area of Yakutsk city. We performed phase optimization using adaptive coherence estimation to reduce the impact of vegetation. We then used StaMPS technology to generate surface deformation time series, allowing us to analyze the spatiotemporal distribution of seasonal deformation during freezing and thawing. The research findings indicate the boreal forest region in central Yakutia displays InSAR displacement signal in association with surface uplift caused by freezing of the active layer while urban area shows resilience against the melting permafrost.

Interferometric synthetic aperture radar (InSAR) has been widely applied in geoscience. As a fundamental parameter, coherence provides a quantitative measure of the interferometric phase quality and ground surface change between two SAR acquisitions. Unfortunately, the sample-estimated coherence is often biased due to the signal inhomogeneity and the bias of the estimator, especially when the numbers of SAR images and spatial samples are limited. In this study, we develop a hybrid method to improve the accuracy of coherence estimation. The effect due to heterogeneous pixels is suppressed by homogeneous pixel selection using Kullback-Leibler divergence. The bias caused by the sample magnitude estimator is mitigated through an iterative M-estimation. Experimental results from both simulation and real data tests demonstrate that the new method works well at texture-significant areas with insufficient SAR images.

The application of multitemporal interferometric synthetic aperture radar (MTInSAR) technology in bridge structural health monitoring often encounters considerable challenges due to the intricate nature of bridge structures. Notably, the thermal expansion and contraction (TEC) of bridges can lead to prominent interferometric phase jumps at the expansion joints. When the magnitude of the phase jump exceeds$\pi $, the continuity assumption required for phase unwrapping is no longer valid. Consequently, classical phase unwrapping methods fail to accurately retrieve bridge deformation. To address this limitation, we propose an adaptive MTInSAR method that can partition the bridge into independent segments and concurrently estimate deformation from multiple reference points. The algorithm first identifies expansion joint locations using a mean square error threshold. Subsequently, reference point selection and segmental phase unwrapping are performed to derive displacement time series of persistent scatterers (PSs), where the mechanical properties of the bridge structure are considered. We validate the effectiveness of the method using 23 TerraSAR-X (TSX) images of the Shanghai Yangtze River Bridge. The results demonstrate the successful detection of expansion joints and reliable phase unwrapping in PS subnetworks. Moreover, a comparative analysis with the classical minimum cost flow (MCF) method highlights the superior adaptability and reliability of the proposed approach. Finally, threshold values for triggering conditions when phase jumps occur are quantified. The proposed work will enhance the robust monitoring of bridge motions, safeguarding the structural health of bridges.

Coherence estimation is crucial in Interferometric Synthetic Aperture Radar (InSAR) for various applications, including land cover classification, change detection, and multi-temporal InSAR techniques. However, estimation challenges often arise due to systematic phases caused by deformation and topography, leading to an underestimation. Existing FFT-based stripe correction methods have limitations in handling complex patterns and are noise-sensitive. We propose here a novel approach using local phase surface fitting to remove the trend component and thereby improve the accuracy of the coherence estimation. The algorithm involves adaptive window size selection based on varying phase patterns and joint estimation of surface model parameters using regional network adjustment. Comparative experiments with simulated and Sentinel-1A data demonstrate the superiority of the method in effectively removing trend components, especially from nonlinear stripe regions. This improvement is evident in the significantly enhanced average coherence, with increases of 37.4% and 60.1% observed in the two experimental areas, resulting in enhanced quality of interferogram coherence maps.

In multitemporal interferometric synthetic aperture radar (MTInSAR) processing, phase unwrapping (PhU) is a vital procedure, which affects the accurate deformation retrieval, especially in environments suffering decorrelation. Although a great surge of work focuses on solving the integers of$2\pi $associated with the wrapped observations (i.e., the inputs of unwrapping algorithms), the quality of inputs deserves an equal attention, as it is a basis for a reliable unwrapping. In this letter, we seek to improve the PhU accuracy by enhancing the entire quality of differential phase observations. To select an optimal interferogram stack efficiently, we propose a quadtree-based coherence estimation method for fast evaluating the interferogram quality and then develop a strategy that constructs redundant networks connecting synthetic aperture radar (SAR) images and points, respectively. The proposed strategy utilizes the combination of minimum spanning tree (MST) and triangular closure to determine the pair of image/point included in the network. We validate the effectiveness of our method by Sentine-1 SAR data over Shenzhen airport, where decorrelated scatterers with weak backscattering energy in runways challenge a reliable subsidence extraction. The cross-comparison indicates the importance of the quality of inputs that affects the point connectivity in both temporal and spatial dimensions.

Differential interferometric synthetic aperture radar (DInSAR) has been widely used as one of the most important technologies for determining coseismic deformation. However, DInSAR processing is often perturbed by errors including atmospheric effects and those in topographic models, SAR satellite orbits, and phase unwrapping operation. These nuisance components can degrade the accuracy of the measurements and, therefore, distort the inversion of fault slips especially for moderate earthquakes. We propose in this letter a multitemporal InSAR (MTInSAR) method aiming to accurately determine coseismic deformation. By jointly analyzing a set of preseismic SAR images and one postseismic image, the method allows effective separation of coseismic deformation from topographic and satellite orbital errors deformation based on the distinct spatio-temporal characteristics of the signals. Since the solution is achieved directly from wrapped differential phases, the retrieved deformation is also immune to phase unwrapping errors. The October 6, 2008 Mw 6.3 Dangxiong, China earthquake is studied with the proposed method as an example. The slip inverted, respectively, from the MTInSAR and DInSAR coseismic deformation measurements shows up to 34-cm differences, indicating that the topographic error and inaccurate removal of orbital errors can bias the fault slip inversion.

Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data.

Multiple temporal InSAR (MTInSAR) is a useful tool that has been widely used in slow-moving landslides detection and monitoring. However, the serious decorrelation can affect the scatterer selection and phase unwrapping during the procedures of MTInSAR when the amount of SAR images is small. In this study, a modified StaMPS-SBAS is provided to time-series deformation monitoring for landslides detection and monitoring. Multilook and non-local filtering operations are adopted to improve the coherence and phase quality before StaMPS-SBAS. The time-series deformation is validated with three collected GPS stations. The results found that: (1) Several landslides with average rate over 40mm/yr in the research of interest are observed. (2) The reginal maximum rate reached to - 92.7mm/yr. (3) The RMSE of time-series deformation in the selected GPS station is 4.4 mm.

Tatun volcano group, the last active volcano in Taiwan, is located in northern Taipei Basin, only 15 km north of the Taipei City. It was previously thought to be a dead volcano, but recent studies show that the last magmatic eruption happened about 5000 to 6000 years ago. The geothermal and seismic activities over the Tatun volcanic area have been highly active in recent years. The geochemical analysis implies the potential existing of the magma chamber under the ground surface of northern Taiwan which has the possibility of re-eruption in the future. In this study, we use ALOS-1/PALSAR images to monitor the surface deformation at the Tatun volcanic area. Stratified atmospheric delay and orbit errors are well considered and corrected by an adaptive patch-based method. The derived displacement history provides a detailed map of surface change with large spatial extent, which is validated by GPS measurements. The results demonstrate the capability of InSAR technique to monitor surface deformation over the volcanic zones.

Tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) measurements are mainly caused by spatial and temporal variation of pressure, temperature, and humidity between SAR acquisitions. These delays are described as one of the primary error sources in InSAR observations. Although independent atmospheric measurements have been used to correct TDs, their sparse spatial or temporal resolution requires interpolation, leading to uncertainties in the corrected interferograms. The performance of the conventional phase-based correction method is weakened by the presence of confounding signals (e.g., TDs, deformation, and topographic errors) and spatial variability of the troposphere. Here, we propose a method that can simultaneously estimate stratified TDs together with parameters of deformation and topographic error based on their distinct spatial-temporal correlation. Spatial variability of the relationship between TDs and topographic height is addressed through localized estimation in windows divided by quadtree according to height gradient. We demonstrate the performance of the proposed method with both simulated and real data sets. In addition, both advantages and disadvantages of this method are addressed.

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no a priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

Removing the topographic component in the interferometric synthetic aperture radar (InSAR) phase is conventionally conducted using an external digital elevation model (DEM). However, with an increasing spatial resolution of SAR data, the external DEM is becoming less qualified for this purpose, resulting in notable phase residues and even decorrelation in differential interferograms. Although topographic residuals can be parameterized and estimated by multi-temporal InSAR (MTInSAR) techniques, its accuracy is limited by several factors. Instead of providing accurate height information, shortening the length of baselines is an alternative for DEM phase mitigation. We propose here an MTInSAR processing framework that can retrieve the deformation time series without the estimation of topographic residuals. Within the framework, we generate a set of pseudo interferograms with near-zero baselines by integer combination and take these pseudo interferograms as observations of MTInSAR model, where deformation becomes the only signal that needs to be parameterized. The deformation time series is then retrieved directly from wrapped phases by ridge estimation with an integer ambiguity detector. It is noted that although atmospheric artifacts might be magnified during the combination, their differential components at arcs constructed with neighboring points that are not significantly enlarged. The proposed method is particularly suitable for infrastructure deformation monitoring in urban areas where no accurate external DEM is available. It also has promising potential for retrieving deformation from SAR data stacks with short acquisition intervals since the combination can enlarge the signal of interests in pseudo-observations. Semisynthetic and real data tests indicate that the proposed method has satisfied performance on DEM error mitigation and deformation time series estimation.

Conventional synthetic aperture radar (SAR) image formation requires uniform sampling along the azimuth direction. These image formation relies on frequency domain algorithms including the Range-Doppler Algorithm (RDA), Chirp-Scaling Algorithm (CSA) [1] etc, that make use of azimuth Fast Fourier Transform, hence the requirement of uniform sampling. While uniform sampling in space is not always achievable, the samples can be interpolated onto a uniform grid given the sampling rate is Nyquist. This paper studies the effect of SAR image formation when the azimuth sampling is not uniform, and not Nyquist in some interval. The result showed that even if there is a wide interval in the middle of the synthetic aperture with no samples, an image can still be formed. This result could be useful when the SAR antenna is a phased array, or when the hardware is generating bursts of pulses followed by a quiet window.

Stratified tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) result from the temporal variation of vertical stratification in the lower part of the troposphere. Although an approximately model can be made by assuming a linear relationship between topography and delayed phase in the interferogram, the estimation is weakened by the spatial variability of troposphere and the interference from other confounding signals (e.g., deformation, topographic error and orbit error, etc.). In this contribution, a jointly tropospheric correction scheme is proposed to simultaneously estimate stratified tropospheric delays with deformation and topographic errors. Spatial variability of tropospheric properties is addressed through a localized estimation which is derived by quadtree segmentation according to height gradient. The performance of the proposed method is validated and compared with the conventional linear and weather-model-based methods using Sentinel-1 dataset.

Ground based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of highly dynamic target with sub-millimetric accuracy, especially in man-made structures, e.g. buildings, towers, dams and bridges. In this case study, we use a real aperture radar system, the Gamma Portable Radar Interferometer (GPRI-II), to perform near-real-time deformation monitoring of the deck (back side) of a cable-stayed bridge. As a test site, the Ting Kau Bridge at Tsuen Wan, Hong Kong, was continuously measured from two modes of observation, rotated azimuth scanning (RAS) and fixed azimuth scanning (FAS). The results reveal the wind-driven and vehicle-driven non-uniform oscillation of the bridge. The presented works demonstrate the ability of GPRI-II in bridge deformation or oscillation monitoring, which provide a new way for structural health monitoring of bridge.

In Cognitive Radio Networks (CRNs), a fundamental operation for the secondary users (SUs) is to establish communication through choosing a common available channel at the same time slot, which is referred to as rendezvous. In this paper, we study fast rendezvous for two SUs.