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Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Interferometric Synthetic Aperture Radar (InSAR) technology is capable of detecting large areas of potentially unstable slopes. However, traditional time-series InSAR methods yield fewer valid measurement points (MPs) in alpine canyon regions. Distributed Scatterer (DS) Interferometry (DSI) technology serves as a potent tool for monitoring surface deformation in complex land cover areas; nonetheless, it grapples with high computational demands and low efficiency when interpreting deformation across extended time series. This study proposes an image compression–based DSI (ICDSI) method, which, building upon the DSI method, utilizes principal component analysis (PCA) to compress multi-temporal SAR images in the time dimension. It develops a module for compressing long-time sequence SAR images, acquires the compressed image (referred to as a virtual image), and integrates the developed image compression module into the DSI data processing flow to facilitate the inversion of long-time sequence InSAR land surface deformation information. To validate and assess the credibility of the ICDSI method, we processed a total of 78 ascending and 81 descending scenes of Sentinel-1A images spanning the period 2019–2021 using Small Baseline Subset (SBAS), DSI, and the ICDSI method proposed in this paper. Subsequently, these methods were applied to detect landscape displacements on both coasts of the Jinsha River Basin. The investigation reveals that the ICDSI method outperforms SBAS and DSI significantly in monitoring landslide displacements, enabling the detection of more measurement points (MPs) while utilizing less raw data. The accomplishments of this research program carry crucial theoretical implications and practical application value for the detection of surface deformation using long-time series InSAR. [ABSTRACT FROM AUTHOR]

The recent subsidence at Joshimath in the Indian State of Uttarakhand led to the displacement of thousands of residents. Large cracks developed in the buildings and on the ground. No extensive and comprehensive deformation measurement of this event has been made. In this study, we use both PS and PSDS InSAR time series to investigate the magnitude, spatiotemporal as well as kinematic evolution of this slow-moving landslide. Eighty-seven ascending Sentinel-1 scenes with a temporal baseline of 1056 days from 2020 to 2023 were stacked for interferometric analysis. StaMPS is employed to identify PS points by their amplitude and phase information. TomoSAR is utilized to stipulate a coherence matrix to form a dense PSDS network of interferograms to surge point density for suitable phase unwrapping. PS and DS points are coupled to develop slope velocity maps revealing mean displacement rates of –84 mm for PS and –107 mm for PSDS, respectively. Cross-section profiles drawn on the slopes of subsidence show target scatterers on CS1, CS2 and CS4, yield a cumulative displacement of 400 mm in the last 3 years. CS3 and CS5 show a total displacement of about 350 mm. This study applies PSDS time-series InSAR to decipher ground movement in traditionally decohered environments. It also seeks to establish the boundaries and intensity of subsidence to aid in the mitigation of failure progression. [ABSTRACT FROM AUTHOR]

Interferometic synthetic aperture radar (InSAR) technology has shown great application potential in monitoring geological disasters in mining areas. However, it is difficult for conventional time‑series InSAR technology to detect enough radar targets in mining areas covered by vegetation, which causes underestimation or inaccurate estimation of deformation results. Aiming at the scarcity of measurement points in complex mining areas, the distributed scatterer InSAR analysis method can effectively increase the number of measurement points in the vegetation covered area, thereby accurately describing the temporal evolution and spatial distribution characteristics of deformation in mining areas. The long‑term and high‑intensity mining activities will cause the surface deformation of the goaf, posing serious threats to sustainable mining, infrastructure construction and the safety of lives and property. In order to understand the mining situation in the goaf of Puhe Coal Mine in Shenyang, based on InSAR high‑precision monitoring data, the key underground mining parameters in the mine area are obtained through geophysical modeling inversion. The deformation field simulated based on the optimal parameters is consistent with the InSAR monitoring results, and the inversion mining parameters conform to the actual mining situation. Combining InSAR monitoring results with Okada model to invert mining area parameters can truly reflect the actual mining situation, accurately describe the surface deformation of goaf, and provide important information for scientific formulation of underground mining plans and sustainable development of the mining area. [ABSTRACT FROM AUTHOR]

Time-series SAR interferometry (InSAR) combining permanent scatterer and distributed scatterer (DS), has been strongly developed in subsidence monitoring. It is known that the Component extrAction and sElection SAR (CAESAR) is a recently presented approach of selecting and filtering scattering mechanisms for DS. This article proposes an improved CAESAR algorithm in InSAR processing. Phase optimization is performed by eigenvalue decomposition and principal component analysis of the coherence matrix that constructed based on the identified homogeneous pixels. In addition, only the interferometric phases with low noise are used to calculate the goodness-of-fit value. The improved method has been tested for subsidence monitoring over a nonurban area located in Xiongxian, China using 25 Sentinel-1A images. The results show that the improved method can provide the high spatial density of deformation measurements with accuracy ensured. Noticeable land subsidence is revealed widely within the north of Xiongxian county, particularly in Daying, Mijiawun and Beishakou towns. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Pekinensis is the property of Editorial Office of Acta Scientiarum Naturalium Universitatis Pekinensis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sentinel−1 (S1) data enables effective monitoring of displacements using persistent scatterer interferometry (PSI). S1 includes VV and VH polarization channels, allowing us to apply polarimetric techniques to PSI. In short, polarimetric PSI (PolPSI) exploits the available polarization channels to enhance the identification and processing of measurement points including persistent scatterers (PS) and distributed scatterers (DS). Previous works have shown the benefits of using PolPSI for PS points with S1 data, but the corresponding analysis for DS is missing.DS points are processed by finding a neighborhood of statistically homogeneous pixels (SHP) and averaging the phase within that neighborhood. In this work we show how dual-polarimetric data are stricter on the selection of the SHP group than single-polarimetric data. Thanks to the information added by the second channel, different land covers are not mixed in the SHP group. As a result, the number of points in the SHP groups is generally smaller than with VV alone, but they are more reliable. The impact of this strategy on the resulting deformation estimates is also investigated in this work, showing that the deformation areas are fully preserved and the influence of nearby pixels associated with other scene elements is avoided.

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes. [ABSTRACT FROM AUTHOR]

Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm. [ABSTRACT FROM AUTHOR]

Distributed scatter (DS) interferometric synthetic aperture radar is a powerful technology for analyzing displacements of the earth's surface. Unfortunately, the preparatory step of DS pre-processing is enormously time consuming. The present research puts forward a deep learning-based approach called Distributed Scatterers Prediction Net (DSPN), that can reduce the computational load considerably. DSPN is a convolutional neural network, which generates DS candidate masks based on nine input layers. Masked pixels with low prospect of being DS are omitted during DS pre-processing. Tests on 6 different terrains in North Rhine-Westphalia and Sicily with Sentinel-1 data show that DSPN saves 11% to 87% computation time depending on the scene without significantly reducing coverage with information. Our experiments show that the proposed approach can effectively predict DS candidates and speeds up processing, indicating its potential for analyzing the big data of remote sensing. To the best of our knowledge, this is the first attempt to do a classification in DS candidates and non-DS candidates as a preparatory step to DS pre-processing.

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes.

The Gongjue area in Jinsha River Basin is located in the mountainous region, SW China, with the development of geological hazards such as landslides and ground fissures, which bring the serious threat to the lives and properties of people along the route, and the construction and safe operation of infrastructure such as transportation. Thus, there is an urgent need to conduct a wide-area monitoring and analysis of the recent development characteristics of landslides and ground fissures in the area. The DS-InSAR technology with both high-coherence points and distributed targets is employed to obtain the high-precision spatial distribution information of surface in the LOS direction with 64 scenes in ascending orbit and 69 scenes in descending orbit of Sentinel-1 image, which are acquired from February 2019 to July 2021. 22 obvious geological hazards are detected, and the maximum deformation rate of-254 mm·a-1 along the LOS direction is observed on the landslide near Shadong town. And then, the spatial and temporal characteristics of typical landslides and ground fissure deformation in the area were also analyzed based on the deformation along its steepest slope direction and precipitation data. The results indicate that landslide deformation is seriously affected by complex topography, and ground fissure deformation yields a good spatial correlation with its strike direction, and precipitation aggravates the activity of some geological hazards. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to effectively detect the detailed subsidence of tattered ground surface composed of many small fragments with the distributed scatterer interferometric synthetic aperture radar (DSInSAR) technique, a fast and accurate distributed scatterer extraction (FADSE), as an improved distributed scatterers extraction algorithm, is proposed and demonstrated in this paper. The emphasis of FADSE is on the improvement of accuracy of extracted DSs and detection efficiency as well. For the purpose, nonparametric estimation and parametric estimation methods are combined into FADSE to fast identify as many accurate statistically homogeneous pixels (SHP) as possible. Then the thresholds of homogeneous pixel number and coherence coefficient are adjusted to select DSs from SHPs. The validation of FADSE was performed in the case of loess subsidence detection in Tongren county, Qinghai Province of China, using 20 Sentinel-1A SAR images acquired between February 2016 and June 2017. Moreover, FADSE was compared with the Kolmogorov-Smirnov algorithm and Fast Statistically Homogeneous Pixel Selection method. Results show that FADSE is capable of efficiently extracting more DSs that are accurate and the detailed subsidence of tattered ground surface can be accurately detected.

The Zongling landslide (Nayong, Guizhou, China) is dominated by a unique karst landscape area with many landslide masses. In this paper, an improved Interferometric Point Target Analysis (IPTA) method is proposed to identify and monitor the Zongling landslide. In this method, the Anderson-Darling test is applied to distributed scatterer (DS) selection, and DS and persistent scatterer (PS) are combined to improve the density of measurement points in vegetation area. Moreover, this method is also characterized by the appropriate combination of differential interferograms produced by a small baseline subsets and the employment of the phase triangulation algorithm to estimate the optimal phase. Combining 105 scenes of C-band Sentinel-1A ascending and descending data acquired during 2014–2018, the method is applied to retrieve time series displacement for the large-scale landslide in Zongling Town. Finally, three accelerating landslides are identified from our result, which is consistent with ALOS PALSAR differential interferometry synthetic aperture radar (DInSAR) results and field investigation. The influencing factors and deformation mechanism of the Zongling landslide are also analysed. Our monitoring results will help the local government to conduct regular inspections and strengthen disaster prevention in mountain areas. [ABSTRACT FROM AUTHOR]

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness.

After the deadly Ms 8.0 Wenchuan earthquake, the Wenjiagou landslide produced steep topography, a narrow gully and abundant loose sediments; these factors have contributed to the high debris flow risk in the Wenjiagou area during subsequent rainy seasons. At least five debris flows have occurred in the Wenjiagou area between September 24, 2008, and September 18, 2010, which resulted in seven casualties and an economic loss of approximately 446 million RMB. To reduce the risk of debris flows and landslides, the Wenjiagou Valley Debris Flow Control Project (WVDFCP), which cost over 2 billion RMB, was carried out and completed in 2011. The control measures of the project effectively reduced the scale and damage of the following debris flows. In this paper, the recent deformation of the giant landslide and its effect on the WVDFCP are evaluated by applying a time-series interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) to the Radardat-2 SAR data collected from June 2014 to September 2015. In addition, the experimental results show that most areas of the landslide are stable, with an average deformation rate of less than 5.0 mm/year. The results demonstrate that the control measures of the WVDFCP not only reduced the damage caused by the later debris flows but also contributed to the consolidation of the loose sediments in the Wenjiagou landslide area. The time-series InSAR technique based on the DSs of high-resolution SAR images is an important tool for deformation monitoring of earthquake-induced landslides. [ABSTRACT FROM AUTHOR]

We describe a python package for nonlinear phase linking of full resolution SAR images using both distributed and persistent scatterers. In the workflow, the first step is to find for each pixel the set of self-similar pixels in order to identify persistent and distributed scatterers. Next the phase linking is performed using the full complex coherence matrix containing the wrapped phase values of each distributed scatterer. Our package uses a hybrid approach consisting of eigenvalue decomposition-based maximum likelihood phase linking and the classic eigenvalue decomposition method. The latter is used for pixels with a non-invertible covariance matrix. A sequential mode achieves computational efficiency. The next step is to unwrap the phase by selecting an optimum unwrapping network of interferograms and invert for the unwrapped phase time-series which is converted to the displacement time-series. We show how the performance of phase linking depends on the temporal correlation behavior using simulations of the coherence matrix. The sequential approaches better retrieve the simulated phases compared to the non-sequential approaches for all temporal coherence models. Phase linking methods retrieve the simulated phase with residuals close to the Cramér–Rao lower bound for coherent seasons where the absolute values of coherence matrix are high and provide a tool for obtaining InSAR measurements over areas with seasonal snowfall. We furthermore show that unwrapping errors propagate differently depending on the unwrapping network. For single-reference networks there is no error propagation, but for sequential networks it compromises the accuracy of the final displacement time-series. Delaunay networks provide an optimum solution in terms of accuracy and precision if there are several years of data with frequent temporal decorrelation or strong seasonal decorrelation. We present applications using Sentinel-1 data in different natural and anthropogenic environments. • A python package for nonlinear phase linking of SAR images is provided. • Phase linking provides InSAR measurements over areas with seasonal snowfall. • Unwrapping errors propagate differently depending on the unwrapping network. • Delaunay networks are preferred for unwrapping the seasonally decorrelated areas. [ABSTRACT FROM AUTHOR]

Coastal wetland ecosystems are among the most productive yet highly threatened systems in the world, and population growth and increasing economic development have resulted to extremely rapid degradation and loss of coastal wetlands. Spaceborne differential Interferometry SAR has proven a remarkable potential in wetland applications, including water level monitoring in high spatial resolution. However, due to the absence of ground observations for calibration and validation, long term monitoring of water depth, which is essential to evaluate ecosystem health of wetlands, is difficult to be estimated from spaceborne InSAR data. We present a new differential synthetic aperture radar method for temporal evolution of water depth in wetlands. The presented technique is based on distributed scatter interferogram technique in order to provide a spatially dense hydrological observation for coastal wetlands, which are characterized by high temporal decorrelation. This method adapts a strategy by forming optimum interferogram network to get a balance between maximum interferometric information preservation and computational cost reduction, and implements spatial adaptive filtering to reduce noise and enhance fringe visibility on distributed scatterers. Refined InSAR observation is tied to absolute reference frame to generate long term high resolution water level time-series using stage data. We transform water level time-series to long term observation of water depth with assistance of a dense measurement network of water depth. We present water depth time-series obtained using the data acquired from 2007 to 2010 by the ALOS satellite, which supplied significant information to evaluate ecological performance of wetland restoration in the Yellow River Delta. [ABSTRACT FROM AUTHOR]

Persistent scatterer (PS) interferometry (PSI) techniques are designed to measure ground deformations using satellite synthetic aperture radar (SAR) data. They rely on the identification of pixels not severely affected by spatial or temporal decorrelation, which, in general, correspond to pointlike PSs commonly found in urban areas. However, in urban areas, we can find not only PSs but also distributed scatterers (DSs) whose phase information may be exploited for PSI applications. Estimation of DS parameters requires speckle filtering to be applied to the complex SAR data, but conventional speckle filtering approaches tend to mask PS information due to spatial averaging. In the context of single-polarization PSI, adaptive speckle filtering strategies based on the exploitation of amplitude temporal statistics have been proposed, which seek to avoid spatial filtering on nonhomogeneous areas. Given the growing interest on polarimetric PSI techniques, i.e., those using polarimetric diversity to increase performance over conventional single-polarization PSI, in this paper, we propose an adaptive spatial filter driven by polarimetric temporal statistics, rather than single-polarization amplitudes. The proposed approach is able to filter DS while preserving PS information. In addition, a new methodology for the joint processing of PS and DS in the context of PSI is introduced. The technique has been tested for two different urban data sets: 41 dual-polarization TerraSAR-X images of Murcia (Spain) and 31 full-polarization Radarsat-2 images of Barcelona (Spain). Results show an important improvement in terms of number of pixels with valid deformation information, hence denser area coverage. [ABSTRACT FROM PUBLISHER]

Abstract: In a recent contribution Ferretti and co-workers (Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR IEEE Transactions on Geoscience and Remote Sensing 49(9), pp. 3460–3470) have proposed the SqueeSAR method, a way to exploit temporally coherent distributed scatterers in coherent SAR data stacks. Elevation and deformation or subsidence estimates are obtained with accuracy similar as in the well known persistent scatterer interferometry (PSI). In this paper we propose an alternative approach and provide a first demonstration of the optimal estimation of distributed scatterers’ phase histories in urban areas. Different to SqueeSAR, we derive phase histories for each distributed scatterer pixel rather than for groups of pixels. We use the Anderson–Darling statistical test to identify neighboring samples of the same distribution. Prior to covariance matrix estimation required for maximum likelihood estimation we apply a multi-resolution defringe technique. By using TerraSAR-X high resolution spotlight data, it is demonstrated that we are able to retrieve reliable phase histories and motion parameter estimates from distributed scatterers with signal-to-noise-ratio far below the common range. [Copyright &y& Elsevier]

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness. [ABSTRACT FROM AUTHOR]

Phase analysis based on high-quality pixel (HQP) is crucial to ensure the measurement accuracy of ground-based SAR (GB-SAR). The amplitude dispersion (ADI) criterion has been widely applied to identify pixels with high amplitude stability, i.e., permanent scatterers (PSs), which typically are point-wise scatterers such as stones or man-made structures. However, the PS number in natural scenes is few and limits the GB-SAR applications. This paper proposes an improved method to take HQP selection applied for natural scenes in GB-SAR interferometry. In order to increase the spatial density of HQP for phase measurement, three types of HQPs including PS, quasi-permanent scatter (QPS), and distributed scatter (DS), are selected with different criteria. The ADI method is firstly utilized to take PS selection. To select those pixels with high phase stability but moderate amplitude stability, the temporal phase coherence (TPC) is defined. Those pixels with moderate ADI values and high TPC are selected as QPSs. Then the feasibility of the DS technique is explored. To validate the feasibility of the proposed method, 2370 GB-SAR images of a natural slope are processed. Experimental results prove that the HQP number could be significantly increased while slightly sacrificing phase quality. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used. [ABSTRACT FROM AUTHOR]