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This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of $1.82\,\,\text {m}\cdot \mathrm {s}^{-1}$ ], followed by subswath 2 (RMSE of $1.92\,\,\text {m}\cdot \mathrm {s}^{-1}$), while subswath 1 showed the lowest retrieval accuracy (RMSE of $2.37\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). The average RMSE of wind speeds retrieved from all the three subswaths is $2.08\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$ under low-to-high wind speed regimes (wind speeds $< 25\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). We further observed that the dependence of VH-polarized normalized radar cross section (NRCS) on incidence angle is attributable to the high and changing noise equivalent sigma zero (NESZ) with incidence angle under low-to-moderate wind speed regimes. And that strong VH-polarized radar signals could overcome the NESZ effect, thereby eliminating the dependence of VH-polarized NRCS on incidence angle under strong wind conditions. For Sentinel-1 IW mode VH-polarized data, the effect of NESZ could be ignored when wind speeds are greater than $15\,\,\text {m}\cdot \mathrm {s}^{-1}$ , as a better wind speed retrieval performance of these data has been recorded in this paper at wind speeds greater than $10\,\,\text {m}\cdot \mathrm {s}^{-1}$ , owing to an RMSE below $1.6\,\,\text {m}\cdot \mathrm {s}^{-1}$ and biases ranging from −0.5 to $0.5\,\,\text {m}\cdot \mathrm {s}^{-1}$. [ABSTRACT FROM AUTHOR]

This paper focuses on the detection, from a stack of repeated-pass interferometric synthetic aperture radar (SAR) images, of such changes causing a target to completely lose the correlation between one epoch and another. This can be the consequence of human activities, such as construction, destruction, and agricultural activities, and also be the consequence of hazards, such as earthquake, landslides, or flooding, to buildings or terrains. The millimetric sensitivity of SAR makes it valuable for detecting such changes. This paper approaches two coherent change detection methods: a space coherent, time incoherent one and a full space and time coherent one, both based on the generalized likelihood ratiob (LR) test. A preliminary validation of the method is provided by processing two Sentinel-1 data stacks of 2016 Central Italy earthquake and by comparing the results with the map of damaged buildings in Amatrice and Accumoli made by Copernicus Emergency Management Service. [ABSTRACT FROM AUTHOR]

This paper proposed a 3-D surface deformation retrieval algorithm for synthetic aperture radar interferometry (InSAR) based on a Global Navigation Satellite System (GNSS) as a transmitter and a fixed receiver (GNSS-based InSAR), where simultaneous multiple GNSS transmitters and a repeat-pass concept were adopted. This paper consists of three parts. First, the interferometric phase model under repeat-pass concept was established for both general and bistatic permanent scatterer (PS) cases in the GNSS-based InSAR. Second, the 3-D deformation retrieval algorithm was presented, and the position dilution of precision was analytically derived to evaluate the performance of measured 3-D deformation. Third, using a designed transponder onboard displacement device as the bistatic PS, the feasibility of 3-D deformation retrieval using GNSS-based InSAR was confirmed by repeat-pass experiments, where four simultaneously available Beidou-2 Inclined Geosynchronous Orbit satellites were used as transmitters, and 16 repeat-pass data sets were collected. Using the proposed algorithm, the final experimental results suggested that the GNSS-based InSAR could obtain deformation estimations with better accuracy than at 5 mm in all three directions. Thus, huge potential exists for applications such as landslide prediction and infrastructure safety monitoring. [ABSTRACT FROM AUTHOR]

Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents the first experimental demonstration of the virtual bandwidth synthetic aperture radar (VB-SAR) imaging scheme. VB-SAR is a newly developed subsurface imaging technique which, in stark contrast to traditional close proximity ground penetrating radar schemes, promises imaging from remote standoff platforms such as aircraft and satellites. It specifically exploits the differential interferometric SAR (DInSAR) phase history of a radar wave within a drying soil volume to generate high-resolution vertical maps of the scattering through the soil volume. For this study, a stack of C-band vertically polarized DInSAR images of a sandy soil containing a buried target was collected in the laboratory while the soil moisture was varied--first during controlled water addition, and then during subsequent drying. The wetting image set established the moisture-phase relationship for the soil, which was then applied to the drying DInSAR image set using the VB-SAR scheme. This allowed retrieval of high-resolution VB-SAR imagery with a vertical discrimination of 0.04 m from a stack of 1-m vertical resolution DInSAR images. This paper unequivocally shows that the basic principles of the VB-SAR technique are valid and opens the door to further investigation of this promising technique. [ABSTRACT FROM AUTHOR]

Multibaseline (MB) phase unwrapping (PU) is a critical processing step for the MB synthetic aperture radar interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU has wider application scope on the study area with strong phase variation, because it can overcome the limitation of the Itoh condition. Since most of the MB PU methods need to process multiple interferograms simultaneously, the size of the input interferograms will pose unique challenges when it exceeds the limit of computational capabilities. Until now, the research achievements related to large-scale (LS) MB PU have been quite limited. To deal with such case, we propose a technique for applying the two-stage programming-based MB PU method (TSPA) proposed by Yu and Lan to the LS MB InSAR data set in this paper. To be specific, the MB $L^\kappa $ -norm envelope-sparsity theorem is proposed and proved first, which gives a sufficient condition to exactly guarantee the consistency between local and global TSPA solutions. Afterward, based on the MB $L^\kappa $ -norm envelope-sparsity theorem, we put forward an interferogram tiling strategy, whereby each LS interferogram in the input MB InSAR data set is partitioned into a set of several smaller sub-interferograms that can be unwrapped individually by TSPA in parallel or in series. Both theoretical analysis and experimental results show that the proposed tiling strategy is effective for the LS MB PU problem. [ABSTRACT FROM AUTHOR]

Pixel selection is a crucial step of all advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques that have a direct impact on the quality of the final DInSAR products. In this paper, a full-resolution phase quality estimator, i.e., the temporal phase coherence (TPC), is proposed for DInSAR pixel selection. The method is able to work with both distributed scatterers (DSs) and permanent scatterers (PSs). The influence of different neighboring window sizes and types of interferograms combinations [both the single-master (SM) and the multi-master (MM)] on TPC has been studied. The relationship between TPC and phase standard deviation (STD) of the selected pixels has also been derived. Together with the classical coherence and amplitude dispersion methods, the TPC pixel selection algorithm has been tested on 37 VV polarization Radarsat-2 images of Barcelona Airport. Results show the feasibility and effectiveness of TPC pixel selection algorithm. Besides obvious improvements in the number of selected pixels, the new method shows some other advantages comparing with the other classical two. The proposed pixel selection algorithm, which presents an affordable computational cost, is easy to be implemented and incorporated into any advanced DInSAR processing chain for high-quality pixels’ identification. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters’ variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system. [ABSTRACT FROM AUTHOR]

Speckle noise and decorrelation can hamper the application and interpretation of PolSAR images. In this paper, a new adaptive multitemporal Pol(DIn)SAR filtering and phase optimization algorithm is proposed to address these limitations. This algorithm first categorizes and adaptively filters permanent scatterer (PS) and distributed scatterer (DS) pixels according to their polarimetric scattering mechanisms [i.e., the scattering-mechanism-based filtering (SMF)]. Then, two different polarimetric DInSAR (POLDInSAR) phase OPTimization methods are applied separately on the filtered PS and DS pixels (i.e., POLOPT). Finally, an inclusive pixel selection approach is used to identify high-quality pixels for ground deformation estimation. Thirty-one full-polarization Radarsat-2 SAR images over Barcelona (Spain) and 31 dual-polarization TerraSAR-X images over Murcia (Spain) have been used to evaluate the performance of the proposed algorithm. The PolSAR filtering results show that the speckle of PolSAR images has been well reduced with the preservation of details by the proposed SMF. The obtained ground deformation monitoring results have shown significant improvements, about $\times 7.2$ (the full-polarization case) and $\times 3.8$ (the dual-polarization case) with respect to the classical full-resolution single-pol amplitude dispersion method, on the valid pixels’ densities. The excellent PolSAR filtering and ground deformation monitoring results achieved by the adaptive Pol(DIn)SAR filtering and phase optimization algorithm (i.e., the SMF-POLOPT) have validated the effectiveness of this proposed scheme. [ABSTRACT FROM AUTHOR]

Multibaseline (MB) synthetic aperture radar interferometry (InSAR) has the potential to improve the traditional single-baseline InSAR from the ill-posed problem to the well-posed problem. It is because MB InSAR phase unwrapping (PU) can take advantage of the baseline diversity to significantly increase the ambiguity intervals of interferometric phases, so it completely overcomes the limitation of the phase continuity assumption. However, due to the mathematical foundation of most of the MB PU methods, i.e., the Chinese remainder theorem (CRT), has poor measurement bias robustness (measurement bias could be caused by surface deformation, atmospheric artifact, and phase noise), CRT is sensitive to the normal baseline length. In other words, even if we choose the same CRT-based MB PU method, different system baseline lengths could result in different PU performances. Therefore, how to choose the baseline lengths to optimize CRT performance is crucial to all the CRT-based MB PU methods. In this paper, a nonlinear mixed-integer programming-based baseline design criterion (referred to NIP criterion) is proposed to maximize the measurement bias tolerance of the CRT-based MB PU. The optimality condition of the NIP criterion quantitatively provides important instructions for the CRT-based MB PU applications and offers significant guidance in the development of the practical MB InSAR system. The experiment results are shown to verify the effectiveness of the NIP criterion by using three representative CRT-based MB PU methods. [ABSTRACT FROM AUTHOR]

Soil moisture and wet biomass changes between two noninstantaneous SAR observations markedly affect the displacement estimates obtainable with Differential Interferometric Synthetic Aperture Radar (DInSAR). The separation, the modeling of these influences besides their uncoupling from the displacement signal, and the atmospheric disturbances are still unsolved issues for several repeat-pass interferometric applications. This paper focuses on the separation of vegetation changes from the other phase contributions affecting repeat-pass measurements over vegetated areas. These phase terms mainly relate to changes in soil moisture, atmospheric delays, and surface deformation. The separation is achieved with a first-order scattering solution decomposing the observed HH and VV DInSAR phases in the sum of several phase terms. The latter mainly consider the changes in soil surface scattering and in the two-way propagation through a vertically oriented vegetation canopy. No assumption is made on the spatiotemporal evolution of the displacement and atmosphere. The overall approach is tested on a L-band data set acquired over an agricultural area. Upon calibration, the model allows for estimating changes in wet biomass based on the nonzero HH–VV DInSAR phase difference observed over several birefringent agricultural fields. The obtained biomass estimates provide then a correction for the effect of vegetation changes on the observed HH and VV DInSAR phases. Deprived of the vegetation contribution, the remainder phase terms can be more easily explored for further analyses, e.g., the estimation of soil moisture changes and/or surface movements in vertically oriented vegetated areas. [ABSTRACT FROM AUTHOR]

Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain. [ABSTRACT FROM AUTHOR]

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

While polarimetric persistent scatterer InSAR (PSI) is an effective technique for increasing the number and quality of selected PS pixels, existing methods are suboptimal; a polarimetric channel combination is selected for each pixel based either on amplitude, which works well only for high-amplitude scatterers such as man-made structures, or on the assumption that pixels in a surrounding window all have the same scattering mechanism. In this paper, we present a new polarimetric PSI method in which we use a phase-based criterion to select the optimal channel for each pixel, which can work well even in nonurban environments. This algorithm is based on polarimetric optimization of temporal coherence, as defined in the Stanford Method for PS (StaMPS), to identify the scatterers with stable phase characteristics. We form all possible copolar and cross-polar interferograms from the available polarimetric channels and find the optimum coefficients for each pixel using defined search spaces to optimize the temporal coherence. We apply our algorithm, PolStaMPS, to an area in the Tehran basin that is covered primarily by vegetation. Our results confirm that the algorithm substantially improves on StaMPS performance, increasing the number of PS pixels by 48%, 80%, and 82% with respect to HH+VV, VV, and HH channels, respectively, and increasing the signal-to-noise ratio of selected pixels. [ABSTRACT FROM AUTHOR]

This paper presents a nonlocal interferometric synthetic aperture radar (InSAR) filter with the goal of generating digital elevation models (DEMs) of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on what level of noise reduction can be achieved. The proposed filter is specifically designed to account for the inherent diversity of natural terrain by setting several filtering parameters adaptively. In particular, it considers the local fringe frequency and scene heterogeneity, ensuring proper denoising of interferograms with considerable underlying topography as well as urban areas. A comparison using synthetic and TanDEM-X bistatic strip map data sets with existing InSAR filters shows the effectiveness of the proposed techniques, most of which could readily be integrated into existing nonlocal filters. The resulting DEMs outclass the ones produced with the existing global TanDEM-X DEM processing chain by effectively increasing the resolution from 12 to 6 m and lowering the noise level by roughly a factor of two. [ABSTRACT FROM AUTHOR]

The three-pass differential synthetic aperture radar interferometry (DInSAR) is one of the approaches in radar interferometry applications for measuring the deformation of the earth surface. The conventional three-pass DInSAR needs a successful single-baseline (SB) phase unwrapping (PU) procedure on each interferogram to ensure accurate deformation monitoring. Because of the limitation of the phase continuity assumption, the SB PU becomes a challenging processing step when the study area has strong phase variation. In this paper, the multibaseline (MB) InSAR PU methodology, which can eliminate the phase continuity assumption by means of the baseline diversity, is transplanted into the conventional three-pass DInSAR domain. Based on MB PU, a novel terrain deformation estimation approach is developed. Both theoretical analysis and experimental results demonstrate that the proposed method is an effective surface deformation estimation method. [ABSTRACT FROM AUTHOR]

Persistent scatterer (PS) interferometry and more recently synthetic aperture radar tomography have shown to be powerful tools in urban scenarios for providing 3-D point clouds in the reconstruction of buildings as well as in the monitoring of their possible slow temporal deformations. The detection of PSs represents a fundamental aspect, which in the literature has been mainly addressed at full resolution (single-look detection), thus considering only the scatterer coherence properties along the different acquisitions. In this paper, we investigate the benefits offered by the usage of multiple observation looks. Multi-look generalized likelihood ratio test detection schemes are derived and analyzed in terms of detection performances. The analysis shows that even a slight multi-look can provide a dramatic improvement on the detection capability both on simulated and real data, especially in the areas characterized by a low signal-to-noise ratio and in the presence of a limited number of acquisitions. [ABSTRACT FROM AUTHOR]

In this paper, a dual-frequency and dual-baseline (DFDB) processing framework for the extraction of high-precision terrain information from airborne interferometric synthetic aperture radar (SAR) data is presented. Specifically, we propose the use of two single-pass data sets acquired simultaneously in two different frequency bands and two large-baseline repeat-pass data sets also acquired simultaneously in two frequency bands. The configuration profits from the stability of the single-pass derived elevation maps in relation to spatially correlated artifacts as well as from the increased sensitivity associated with large-baseline acquisitions. Moreover, the dual-frequency nature of the data set enables the tackling of the phase unwrapping issue, promoting the retrieval of unambiguous measurements. Several algorithms for the interferometric processing of the DFDB airborne data set are proposed, including the outline of multichannel phase calibration and unwrapping error correction strategies and approaches to remove spatially correlated artifacts and extract the common underlying topography. Elevation models generated from a DFDB data set acquired with the airborne F-SAR sensor over tidal flats in northern Germany are presented, and comparisons with an airborne laser scanner reference show errors with a standard deviation of around 14 cm and a mean absolute deviation of less than 10 cm. [ABSTRACT FROM AUTHOR]

Phase quality is a key element in the analysis of the deformation of the Earth’s surface carried out with differential synthetic aperture radar interferometry. Various decorrelation sources may degrade the surface deformation estimates, and thus, phase filters are needed for this kind of application. The well-known Goldstein filter is the most widely used due to its simple implementation and computational efficiency. In the past years, improved filters have been proposed, which are based on this filter but introduce variations in the data processing. The effectiveness of these filters mostly depends on the size of the filtering window, the weight of the smoothed spectrum, and the kernel used to filter the spectrum. In this paper, we evaluate the performance of four of these filters and present a new method that outperforms all of them. The proposed filter is based on an iterative method in which the original phase is denoised progressively with adaptive filtering windows of different sizes. The effectiveness of the filter is controlled by the interferometric coherence, a direct indicator of the phase quality. Moreover, we introduce some modifications regarding the processing of the power spectrum. Specifically, we propose to smooth the original phase using a new filter which is based on a Chebyshev interpolation scheme. The performance of the new filter has been tested on both simulated and real interferograms, acquired by RADARSAT-2 and the Uninhabited Aerial Vehicle Synthetic Aperture Radar, which mapped two different geological events that caused surface deformation. [ABSTRACT FROM AUTHOR]

This paper deals with the assignment of persistent scatterers (PSs) to buildings represented by a 3-D city model. Affiliating PSs to real-world structures is highly relevant for two reasons. First, it facilitates interpretation of PS deformation estimates and can be even used to improve those estimates using advanced deformation models for complete structures. Second, such an assignment enables the compilation of a PS density map for the structures under investigation. This helps to mitigate the problem of opportunistic sampling, which is one of the major drawbacks of PS interferometry (PSI). Specifically, a framework in which the PSs are geometrically aligned with the city model and assigned to the bounding surfaces of the single models is outlined. Cases where such an assignment cannot be established are attributed to generalization of the city model or to locations where the reflection mechanism inducing the PS does not correspond to a real-world structure. From the assignments, a density map is compiled, and the main factors driving the PS density are identified and discussed: surface structure, shadowing, aspect dependency, and quasi-random effects (e.g., temporary changes due to building renovation activities). The test site for this paper is located in the inner city area of Berlin. PS results of two high-resolution spotlight TerraSAR-X data stacks of different pass directions are used. The buildings are represented by a level-of-detail 2 city model and an airborne light detection and ranging data set. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is a phase-based radar signal processing technique that has been addressed from a polarimetric point of view since the late 1990s, starting with Cloude and Papathanassiou's foundational work. Polarimeric InSAR (PolInSAR) has consolidated as an active field of research in parallel to non-PolInSAR. Regarding the latter, there have been a number of issues that were discussed in an earlier paper from which some other questions related to Cloude's PolInSAR come out naturally. In particular, they affect the usual understanding of coherence and statistical independence. Coherence involves the behavior of electromagnetic waves in at least a pair of points, and it is crucially related to the statistical independence of scatterers in a complex scene. Although this would seem to allow PolInSAR to overcome the difficulties involving the controversial confusion between statistical independence and polarization as present in PolSAR, Cloude's PolInSAR originally inherited the idea of separating physical contributors to the scattering phenomenon through the use of singular values and vectors. This was an assumption consistent with Cloude's PolSAR postulates that was later set aside. We propose the introduction of a multidimensional coherence tensor that includes PolInSAR's polarimetric interferometry matrix Ω12 as its 2-D case. We show that some important properties of the polarimetric interferometry matrix are incidental to its bidimensionality. Notably, this exceptional behavior in 2-D seems to suggest that the singular value decomposition (SVD) of Ω12 does not provide a physical insight into the scattering problem in the sense of splitting different scattering contributors. It might be argued that Cloude's PolInSAR in its current form does not rely on the SVD of Ω12 but on other underlying optimization schemes. The drawbacks of such ulterior developments and the failure of the maximum coherence separation procedure to be a consistent scheme for surface topography estimation in a two-layer model are discussed in depth in this paper. Nevertheless, turning back to the SVD of Ω12, the use of the singular values of a prewhitened version of Ω12 is consistent with a leading method of characterizing coherence in modern Optics. For this reason, the utility of the SVD of Ω12 as a means of characterizing coherence is analyzed here and extended to higher dimensionalities. Finally, these extensions of the concept of coherence to the multidimensional case are tested and compared with the 2-D case by numerically simulating the scattered electromagnetic field from a rough surface. [ABSTRACT FROM PUBLISHER]

In this paper, we contribute to answer the question: How accurately can we estimate heights of buildings with flat roofs given one high-resolution single-pass interferometric synthetic aperture radar (InSAR) image pair and one aerial orthophoto? What makes this problem challenging are the different sensor geometries and the sound stochastic combination of all available elevation cues. We revisit already existing methods and develop novel approaches to determine building heights. A rigorous stochastic approach based on least squares adjustment with functionally dependent parameters is introduced to combine all height measurements per building to one robust height estimate. Observation accuracies of the stochastic model are either taken from the literature or estimated empirically. A major benefit of adjustment is that it delivers a posterior standard deviation per height, which can be interpreted as a precision indicator and is of high relevance for practical applications. Estimated heights of an urban scene are compared to ground truth acquired with airborne laser scanning, allowing us to assess height accuracies that can be achieved under nearly optimal conditions. We conduct statistical tests that validate our model and show that a weighted combination of optical and synthetic aperture radar (SAR) data with least squares adjustment delivers reliable height estimates with meter accuracy for flat-roofed buildings. Additionally, we empirically estimate a confidence interval of the estimated heights that directly tells the user the security margin to be included, for example, in case of building evacuations for an anticipated flooding event, under the condition that the data and model have the same specifications as in this paper. [ABSTRACT FROM PUBLISHER]

This paper focuses on the generation of very high resolution digital elevation models (DEMs) mainly from single-baseline spaceborne synthetic aperture radar (SAR) systems. The stringent requirements on vertical accuracy demand for frequent and accurate measures of the baselines, subject to mechanical vibrations of the boom that may be severe when long booms are exploited. This paper proposes a solution that exploits an existent coarse-resolution DEM, for example, the Shuttle Radar Topography Mission, as a set of ground control points (GCPs). A computational model and an inversion scheme are introduced to jointly take into account the information coming from the grid of GCP and the local shifts measured during image coregistration. The model properly takes account for the quality of the GCP, its location with respect to the target of interest, and the interferogram noise in correspondence to the GCP. A novel “split-swath” approach is proposed to enhance the quality of the baseline estimates. The mode exploits two narrow strips with significant diversity in incidence angle, achieved, for example, by ScanSAR or TOPSAR mode. An example of design is provided by assuming a Ka-band topographic spaceborne SAR mission. [ABSTRACT FROM PUBLISHER]

Measuring 3-D mining-induced displacements is essential to understand mining deformation mechanisms and assess mining-related geohazards. In our previous work, we proposed a method for estimating 3-D mining-induced large displacements with the surface deformation along the radar line-of-sight (LOS) direction derived from a single amplitude pair (SAP) of synthetic aperture radar (SAR) using the offset tracking (OT) procedure (hereafter referred to as OT-SAP). The OT-SAP method effectively reduces the strict requirements on SAR data of the previous OT-based methods for 3-D mining-induced displacement retrieval. However, OT-SAP is not robust to errors in the LOS deformation, due to the lack of redundant observations. In this paper, we present an alternative approach (hereafter called AOT-SAP) to OT-SAP. The AOT-SAP method involves estimating the 3-D mining-induced large displacements with OT-derived 2-D deformation observations along the LOS and azimuth directions from an SAP of SAR, instead of just the LOS deformation in the OT-SAP method. Consequently, more redundant observations are incorporated in the AOT-SAP method compared with the previous OT-SAP method. The theoretical analysis and experiments based on both simulated and real data sets suggest that AOT-SAP can effectively improve the accuracies of the estimated 3-D displacements compared with the OT-SAP-estimated ones. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries’ SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications. [ABSTRACT FROM AUTHOR]

The block-matching 3-D (BM3D) algorithm, based on the nonlocal approach, is one of the most effective methods to date for additive white Gaussian noise image denoising. Likewise, its extension to synthetic aperture radar (SAR) amplitude images, SAR-BM3D, is a state-of-the-art SAR despeckling algorithm. In this paper, we further extend BM3D to address the restoration of SAR interferometric phase images. While keeping the general structure of BM3D, its processing steps are modified to take into account the peculiarities of the SAR interferometry signal. Experiments on simulated and real-world Tandem-X SAR interferometric pairs prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose two methods to optimize scattering mechanism vectors in polarimetric interferometric synthetic aperture radar (InSAR) dealing with high-resolution SAR data. The methods optimize an interferogram by focusing on the pixel-by-pixel variation of the scattering mechanisms. We show the efficacy of the methods using fully polarimetric interferometric radar data obtained from the Advanced Land Observing Satellite-2 PALSAR-2 to generate accurate digital elevation models successfully. Finally, we confirm the importance of considering the polarization in such InSAR systems by comparing parameters optimized in the proposed methods with those calculated from the well-known Pauli coherency matrix. [ABSTRACT FROM AUTHOR]

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

For airborne repeat-pass synthetic aperture radar interferometry (InSAR), precise trajectory information is needed to compensate for deviations of the platform movement from a linear track. Using the trajectory information, motion compensation (MoCo) can be implemented within SAR data focusing. Due to the inaccuracy of current navigation systems, residual motion errors (RMEs) exist between the real and measured trajectory, causing phase undulations in the final interferograms. Up to now, MoCo and RME estimation have usually been combined in airborne InSAR to estimate ground deformation. Conventional MoCo methods generally involve azimuthal and range resampling and phase correction. Then frequency-domain focusing techniques can be used to generate the SAR images. After focusing SAR images with MoCo, both multisquint and autofocus approaches can be used to estimate RME. In addition to the MoCo-based frequency-domain focusing, the time-domain backprojection (BP) technique can also focus the SAR data obtained from highly nonlinear platform trajectories. In this paper, we present, for the first time, the combination of BP and multisquint techniques for RME estimation. A detailed derivation of the implementation of the multisquint approach using the BP-focusing images is presented. Repeat-pass data from the SlimSAR system over Slumgullion landslide are used to demonstrate the feasibility of RME estimation for both stationary and nonstationary scenes. We conclude that the proposed method can effectively remove the RME. [ABSTRACT FROM AUTHOR]

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric-interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone. [ABSTRACT FROM AUTHOR]

Reconstruction of elevation maps from a collection of synthetic aperture radar (SAR) images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data and the 2 phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, PARISAR, that achieves both a resolution-preserving denoising and a robust phase unwrapping (PhU) by combining nonlocal denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based PhU requires solving several issues: 1) computational complexity, the inclusion of nonlocal neighborhoods strongly increasing the number of terms involved during the regularization, and 2) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. PARISAR solves both issues. We compare PARISAR with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multibaseline SAR interferometry. [ABSTRACT FROM AUTHOR]

The TanDEM-X mission is the first free flying bistatic SAR mission. It has the primary objective to generate within a short time frame a global digital elevation model (DEM) of 10-m absolute vertical accuracy and 2-m relative height accuracy. For that, the whole land mass has been mapped at least twice with different baselines. The success of the mission depends on the accuracy of the final DEM and therefore on the reliability of the phase unwrapping (PU) algorithm. Hence, a robust and versatile PU method, which is in accordance with the acquisition concept, is necessary. This paper presents a new method that combines bistatic high-resolution interferometric data in order to perform an accurate PU on a huge amount of data. The dual-baseline PU correction (DB-PUC) framework addresses this challenge by correcting errors that occurred during the single-baseline PU procedure. It benefits from the additional information available through the differential interferogram and the stereo-radargrammetric phase, which are used to correct region-wise the ambiguity bands of the misestimated unwrapped phases to be less sensitive to noise and possible temporal changes. The multilevel of the DB-PUC approach makes it flexible, computationally efficient, and well adapted to deal with the various PU error scenarios. This framework is used operationally for the processing of the data of the TanDEM-X mission. [ABSTRACT FROM AUTHOR]

Compensating the residual motion error (RME) is very important in airborne interferometric synthetic aperture radar (InSAR). In this paper, the wavelet decomposition and polynomial fitting-based (WDPF) method is proposed for detecting and correcting the RME. Wavelet decomposition with root-mean-square error (RMSE) change ratio-based decomposition scale identification is used to detect the RME from the differential interferogram. Polynomial fitting in combination with robust estimation-based least squares is used to absorb the incidence-angle-dependent and topography-dependent components of the RME. A simulated experiment was conducted to test the proposed WDPF method. High-precision RME (with an RMSE of 0.0375 rad) was obtained, which can meet the requirements of InSAR. Real-data L- and P-band InSAR experiments were also performed to test the WDPF method. The results confirmed that the WDPF method can effectively correct the RME for the interferogram. The RMSE of the estimated digital elevation model (DEM) was reduced from 8.03 to 3.46 m and 8.18 to 3.10 m for the L- and P-band interferograms, respectively. Finally, the effects of the external DEM error and polarization on the RME calibration were investigated. The results indicated that the global InSAR DEM products can fulfill the requirement of differential interferogram generation for the WDPF method, and the multipolarization interferograms can help to reduce the effect of the topographic error phase on RME estimation. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) technique is a proven technique for measuring 3-D surface deformations by combining InSAR measurements from different techniques (i.e., differential InSAR, multiaperture InSAR, and pixel offset-tracking) and different tracks (i.e., ascending and descending) on a pixel-by-pixel basis. However, it is difficult to obtain the exact a priori variances or weights for such different kinds of InSAR measurements, resulting in inaccurate estimations of 3-D deformations. This paper proposes a method to retrieve 3-D deformations with InSAR by integrating the strain model and variance component estimation algorithm, which can exploit the spatial correlation of the adjacent points' deformations and produce accurate weights for multiple InSAR measurements. The proposed method is assessed with both simulated and real data sets. The results have shown that the proposed method can accurately measure 3-D surface deformations associated with geohazards, and even those occurring in a transient or short-term period (e.g., earthquake and volcanic eruption). In the case study of the 2007 eruption of Kilauea Volcano (Hawai'i), improvements of 51.2%, 22.4%, and 18.5% have been achieved for the derived east, north, and up displacements, respectively, with respect to those derived from the classical weighted least squares method. [ABSTRACT FROM AUTHOR]

The ionospheric propagation path delay is a major error source in synthetic aperture radar (SAR) interferograms and, therefore, has to be estimated and corrected. Various methods can be used to extract different kinds of information about the ionosphere from SAR images, with different accuracies. This paper presents a general technique, based on a Bayesian inverse problem, that combines various information sources in order to increase the estimation accuracy, and thus the correction. A physically realistic fractal modeling of the ionosphere turbulence and a data-based estimation of the model parameters allow the avoidance of arbitrary filtering windows and coefficients. To test the technique, the differential ionospheric phase screen was estimated by combining the split-spectrum method with the azimuth mutual shifts between interferometric pair images. This combination is convenient since it can benefit from the strengths of both sources: range and azimuth variations from the split-spectrum method and small-scale azimuth variations from more sensitive azimuth shifts. Therefore, the two methods can recover the long and short wavelength components of the ionospheric phase screen, respectively. A theoretical comparison between the Faraday rotation method and the split-spectrum method is also reported. For the use in the combination, precedence was then given to the split-spectrum method because of the comparable precision level, lower susceptibility to biases, and wider applicability. Finally, Advanced Land Observing Satellite Phased Array type L-band SAR L-band images are used to show how the combined result is more accurate than that obtained with the simple split-spectrum method. [ABSTRACT FROM PUBLISHER]

The 3-D deformation retrieval is significant for the accurate evaluation of geologic disasters (e.g., earthquakes and landslides). Multiple-aperture interferometry (MAI) is an effective method to obtain 3-D deformation, combined with the cross-heading tracks synthetic aperture radar (SAR) data. However, because of the limitations of the low earth orbit SAR, a long satellite revisit time, small common areas of the cross-heading tracks data, and the unsatisfied along-track deformation measurement accuracy usually exist in the traditional MAI 3-D deformation retrieval. Geosynchronous SAR (GEO SAR) runs in the geosynchronous orbit, which has the advantages of a large observation area and a short revisit time. This paper focuses on 3-D deformation retrieval by GEO SAR MAI processing. Aiming at the high orbit and the squint looking of GEO SAR, the accurate expressions of the along-track deformation, 3-D deformation, and the errors in GEO SAR MAI processing are given. The distortions and their correction in the MAI interferogram brought by the geometrical difference between the forward- and backward-looking interferograms and the multicycles flat-earth and topographic phases are given. Moreover, an optimal subaperture selection method based on minimum position dilution of precision is proposed. Finally, the effectiveness of the proposed method is validated by simulations and the experiment of BeiDou-2 inclined geosynchronous orbit navigation satellite. The theoretical analysis and the experimental results suggest centimeter-level and even millimeter-level deformation measurement accuracy could be obtained in 3-D by GEO SAR MAI processing. [ABSTRACT FROM PUBLISHER]

One of the main challenges in Interferometric Synthetic Aperture Radar (SAR) is the accurate height reconstruction of the observed scene. Recently, approaches based on Extended Kalman Filter (EKF) have been proposed. Most of them are based on the hypothesis of height profile continuity. Such condition greatly reduces their applicability, being only valid for particular scenarios. Within this paper, we present a novel Kalman-based height reconstruction approach, specifically designed to work with multichannel data related to any type of scenario, both smooth or sharp. The novelty of the technique consists in its ability in detecting and correctly handling sharp height discontinuities while regularizing smooth areas. The approach is able to maintain the high computational efficiency typical of EKF and to work in an almost unsupervised way. The methodology has been tested and validated on both simulated and real X-band (TerraSAR-X and COSMO-SkyMed) high-resolution data sets. Reported results are encouraging and interesting, showing the correctness and the validity of the proposed approach. [ABSTRACT FROM PUBLISHER]

The concept of the “velocity synthetic aperture radar” (VSAR)—a multiaperture sensor capable of measuring radial velocities in the scene and utilizing this information to correct motion-induced imaging distortions inherent to SAR—was proposed two decades ago. Lately, with the emergence of truly multichannel systems featuring antenna arrays with dozens of elements, the approach has been enjoying a renewed interest. The viability and effectiveness of the algorithm were successfully demonstrated in a series of airborne field campaigns that involved imaging both man-made targets and natural maritime features. These experiments and the wealth of resulting data also underscored the need for comprehensive mathematical descriptions of expected target signatures in the collected “image stacks” and for further refinements of the VSAR imaging theory. This paper addresses both tasks by building upon the available mathematical results developed for the along-track interferometric SAR imagery of distributed evolving targets. The approach allows simultaneous accounting for all essential effects known to impact SAR imagery of a target or an extended feature: its azimuth velocity, radial velocity and acceleration, as well as finite coherence time. The emphasis is on obtaining closed-form expressions that could readily illustrate the structure and behavior of the VSAR stack spectrum of such a target and help gauge anticipated focusing improvement stemming from the VSAR image correction. In particular, it is rigorously shown that the VSAR algorithm is successful in situations when SAR defocusing arises predominantly from radial motion and short coherence times—the resulting resolution is generally no worse than that of the corresponding real-aperture radar. On the other hand, strong defocusing due to azimuth translation may be problematic to compensate within the VSAR approach framework. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) in along-track interferometry (ATI) mode has been extensively applied in velocity measurements of ocean currents and ship detection. A notch filter was recently proposed and was demonstrated to be a promising tool for ship detection exploiting quad- or dual-polarization SAR information. In this paper, the investigation of the notch filter is extended to the dual-channel ATI-SAR mechanism for ship detection on the ocean surface. First, a theoretical proof that the interferometric magnitude performs better in ship detection than single-channel amplitude/intensity is given based on the signal-clutter-ratio improvement, which validates the advantages of interferometric SAR against conventional single-channel SAR for the purpose of ship observations on the ocean surface. Second, based on the proof, the version of the notch filter that is suitable for the ATI-SAR, that is, the interferometric notch filter (INF), is proposed. We then analyze the statistical models of the INF distance, which can facilitate the adaptive realization of INF for ship detection in ATI-SAR. Finally, the experimental results for dual-channel ATI-SAR data measured by NASA/JPL L-band AIRSAR verify the accuracy of the theoretical analysis and effectiveness of the INF. [ABSTRACT FROM PUBLISHER]

Two-dimensional phase unwrapping (PU) is a crucial processing step of synthetic aperture radar interferometry (InSAR). With the rapid advance of InSAR technology, the scale of interferograms is becoming increasingly larger. When the size of the input interferogram exceeds computer hardware capabilities, PU becomes more problematic in terms of computational and memory requirements. In the case of “big-data” PU, the input interferogram needs to be first tiled into a number of subinterferograms, unwrapped separately, and then spliced together. Hence, whether the PU result of each subinterferogram is consistent with that of the whole interferogram is critical to the large-scale PU process. To effectively solve this problem, the L^1 -norm envelope-sparsity theorem, which gives a sufficient condition to exactly guarantee the consistency between local and global L^1 -norm PU solutions, is put forward and proved. Furthermore, the L^0 -norm envelope-sparsity theorem, which gives a sufficient condition to exactly guarantee the consistency between local and global L ^0 -norm PU solutions, is also proposed and proved. Afterward, based on these two theorems, two tiling strategies are put forward for the large-scale L^0 -norm and L^1 -norm PU methods. In addition, this paper presents the concepts of the tiling accuracy and the tiling resolution, which are the criteria used to evaluate the effectiveness of a tiling strategy, and we use them to quantitatively analyze the aforementioned tiling strategies. Both theoretical analysis and experimental results show that the proposed tiling strategies are effective for the large-scale L^0 -norm and L^1 -norm PU problems. [ABSTRACT FROM PUBLISHER]

We analyze the methods for measuring azimuth deformation with the L-band Advanced Land Observing Satellite-2 (ALOS-2) scanning synthetic aperture radar (ScanSAR) interferometry. To implement the methods, we extract focused bursts from the ALOS-2 full-aperture product, which is the only product available for ScanSAR interferometry at present. The extracted bursts are properly processed to measure azimuth deformation using interferometric phase. We apply the range split-spectrum method to ScanSAR to estimate the differential ionospheric phase of the interferogram, and take the azimuth derivative of the differential ionospheric phase to mitigate the relative azimuth shift caused by ionosphere. For the first time, azimuth deformation of a large earthquake (April 25, 2015 Nepal earthquake) is nearly completely measured by the L-band ScanSAR interferometry with moderate precision. The result is validated by the azimuth deformation measured by incoherent cross correlation using a pair of high-resolution RADARSAT-2 images. In addition to the final azimuth deformation, we show the possibility of processing full-aperture ScanSAR product using a burst-by-burst approach to form regular interferograms. We also show the recent strong large-scale ionospheric effects on the L-band ALOS-2 ScanSAR interferograms. Other possible applications of this paper include measuring the movement of glaciers. [ABSTRACT FROM PUBLISHER]

Advanced Land Observing Satellite-2 (ALOS-2) is designed to routinely acquire both scanning synthetic aperture radar (ScanSAR) and stripmap data. In this paper, we present a special multiband bandpass filter (MBF) to remove azimuth nonoverlap spectra for the interferometric processing of ALOS-2 full-aperture ScanSAR product. As required by the MBF, we estimate the important ScanSAR system parameters and the start times of raw bursts using ALOS-2 full-aperture ScanSAR image. The resulting MBF can remove the nonoverlap spectra caused by both Doppler centroid frequency difference and burst misalignment. It can be used in ScanSAR–ScanSAR interferometry, as well as ScanSAR–stripmap interferometry. Based on the MBF, we propose a single processing workflow that is able to implement both ScanSAR–ScanSAR interferometry and ScanSAR–stripmap interferometry. Finally, we present example interferograms of the 2015 Gorkha earthquake in Nepal processed using the proposed processing workflow. The interferograms are greatly improved after applying the MBF to remove the significant amount of nonoverlap spectra in the data. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) images are focused via range and azimuth compression. Typically, a matched filter is used for range compression. However, its inherent operation introduces target masking due to sidelobes, reducing the recognition and interpretability of the underlying targets. To optimize the focusing quality, adaptive pulse compression (APC) can be applied. In this paper, APC is assessed for range processing in SAR image focusing. The performance of the method is evaluated with airborne SAR imagery and compared with those of traditional schemes. We show that by applying APC under single-channel interferometric and stepped frequency scenarios, the quality of the SAR images, the corresponding phase maps, and interferometric coherence images can be significantly improved, and that APC provides both better amplitude and phase estimates of the range profiles. APC is shown to preserve the characteristics of the azimuth signals and can be applied without restricting the azimuth compression scheme. [ABSTRACT FROM PUBLISHER]

Interferograms obtained by interferometric synthetic aperture radar generally include many singular points (SPs) originating from interference distortion and noise in the measurement. The filtering process is one of the key techniques in the generation of an accurate digital elevation model (DEM). In this paper, we propose a filter to remove SPs and related distortion using a complex-valued neural network in the spectral domain. It removes SPs nonlinearly and adaptively by referring to the statistics in neighboring windows in the 2-D frequency domain, where the textural features are represented in a more continuous manner than in the real space domain. Experiments demonstrate that the proposed method removes SPs and the distortion in SP-constructing four pixels, namely, the singular unit, more effectively than the conventional filters, resulting in the generation of a more accurate DEM. [ABSTRACT FROM AUTHOR]

In order to enhance the performance of spaceborne synthetic aperture radar–ground moving target indication (SAR-GMTI) systems, multichannel systems with large and preferably nonuniform baselines are required. In this paper, SAR-GMTI algorithms for multichannel SAR systems, which we call multichannel displaced phase center antenna (DPCA), multichannel along track interferometry (ATI), and multichannel DPCA-ATI, are presented. Multichannel DPCA is a deterministic algorithm for clutter and azimuth ambiguity suppression. It successfully suppresses not only uniform azimuth ambiguities but also nonuniform isolated ones, since it does not require uniform clutter covariance assumption as adaptive algorithms do. Multichannel ATI and multichannel DPCA-ATI are the algorithms for target radial velocity estimation. Both of them reduce the target radial velocity ambiguities, which arise with the long baseline systems, by exploiting the multiple receive channel signals. And multichannel DPCA-ATI further achieves robust performance to clutter influence by suppressing the clutter and the azimuth ambiguity in advance. The performances of the proposed algorithms are shown through airborne Ku-band three-channel SAR experiments. It is shown that the multichannel DPCA suppresses strong azimuth ambiguity up to more than 20 dB, and the accuracy of the radial velocity estimation of the multichannel DPCA-ATI is on the order of 0.1 m/s. Furthermore, statistical performance analysis is presented to discuss the potential performance on the spaceborne system. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors. [ABSTRACT FROM AUTHOR]

Persistent scatterer (PS) interferometry analysis has been performed using single-polarimetric synthetic aperture radar (SAR) interferograms, but newer SAR satellites enable the use of polarimetric data for such analyses. In this paper, to analyze polarimetric SAR interferograms, we proposed the maximum likelihood method at the PS selection stage. With the proposed method, interferograms in a certain polarimetric component exhibiting high coherence contribute to the estimation of surface displacements, whereas noisy interferograms of another polarimetric component are less used. The proposed method was examined by analyzing full- and dual-polarimetric interferograms using data obtained from ALOS/PALSAR images in full-polarimetric acquisition mode. The results showed an increase in the number of coherent pixels used to map surface displacements, and that number increased particularly in the analysis of full-polarimetric data. For example, in the case of full-polarimetric interferograms in linear HV basis, the number increased by a factor of 1.8–3.8 compared with those of single-polarimetric interferograms and by a factor of 1.14 compared with a simple integration of the results of single-polarimetric data. Our method increases the accuracy of the detection of the detailed spatial displacement pattern of a displacement phenomenon. [ABSTRACT FROM PUBLISHER]

Using synthetic aperture radar (SAR) interferometry to monitor long-term millimeter-level deformation of urban infrastructures, such as individual buildings and bridges, is an emerging and important field in remote sensing. In the state-of-the-art methods, deformation parameters are retrieved and monitored on a pixel basis solely in the SAR image domain. However, the inevitable side-looking imaging geometry of SAR results in undesired occlusion and layover in urban area, rendering the current method less competent for a semantic-level monitoring of different urban infrastructures. This paper presents a framework of a semantic-level deformation monitoring by linking the precise deformation estimates of SAR interferometry and the semantic classification labels of optical images via a 3-D geometric fusion and semantic texturing. The proposed approach provides the first “SARptical” point cloud of an urban area, which is the SAR tomography point cloud textured with attributes from optical images. This opens a new perspective of InSAR deformation monitoring. Interesting examples on bridge and railway monitoring are demonstrated. [ABSTRACT FROM PUBLISHER]