Positioning for High-Speed Maneuverable Platform Based on Wrapped InSAR Interferogram

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km2 were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification.

Speckle is an inherent characteristic of InSAR (Interferometric Synthetic Aperture Radar) interferogram, it affects the accuracy of prediction and evaluation of urban Geology and Geohazards, so it is necessary to carry out interferogram filtering to suppress the effect of speckle. However, there is a contradiction between speckle reduction and keeping the details of the interferogram in current methods of interferogram filtering. Using the difference idea and the character of the wavelet transform, we propose InSAR interferogram detail-compensating filter method based on the stationary wavelet transform. And an experiment is made on the partial interferogram generated by two SAR images of Bam earthquake with this method. As a result, not only can the new method suppress the speckle effectively, but also it can maintain details of interferogram well by compensating the details, and it reduces the number of residual points greatly.

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR images with wider geographic coverage can be used to monitor ground deformation from local to continental scale. In such a large-scale application scenario, the ocean tide loading displacement introduces a long-wavelength error, which increases with distance, from millimetre to decimetre-level in InSAR interferograms over coastal areas. Despite great efforts being made to investigate the impacts of OTL on InSAR, these works are limited to individual interferograms and seldomly used in time-series analysis. In this study, we fully explore the OTL effects on Sentinel-1 InSAR time-series data along the western coast of the UK. We adopted wavelet analysis to indicate that the OTL displacement creates periodic signals with major cycles of 15 and 64 days under the 6- and 12-day Sentinel-1 sampling rates, respectively. These periodic signals are responsible for high noise magnitude of time-series displacement up to ~1cm and ~1 cm/yr bias on estimated velocities. An example is shown in Figure 1, where large velocity bias (a1-a3) and time-series standard deviation (b1-b3) can be seen along the western coastline of non-OTL corrected deformation fields, which are considerably eliminated after OTL correction. Our further validation against GNSS observations reveals that OTL correction improves the accuracy of large-scale InSAR time-series analysis by 25%.

Ocean tide loading correction for InSAR measurements: Comparison of different ocean tide models

Sentinel-1 observation of the 2017 Sangsefid earthquake, northeastern Iran: Rupture of a blind reserve-slip fault near the Eastern Kopeh Dagh

Interferometric synthetic aperture radar (InSAR) measures surface deformation from repeated passes of satellites or aircraft and has become an important tool to study geophysical phenomena such as earthquakes. However, InSAR data analysis is challenging due to atmospheric water vapor that can mimic the effects of Earth deformation and thus lead to wrong interpretations. We present preliminary results on how to differentiate between tropospheric effects and surface deformation from earthquakes using a convolutional neural network. As earthquake training sets are sparse, our approach leverages transfer learning techniques for tropospheric patterns from areas where deformations are known to be mostly absent over short time periods, and classifies specific areas of interferograms to reflect regions that are dominated by deformation or tropospheric noise. The applicability of the training set to a new area may depend on the similarity of the two climates. Examples of tropospheric delays are shown from interferograms constructed from Sentinel-1 data over part of southern California with short temporal baselines, and examples of deformation are taken from interferograms generated using Okada models. Our classifier is tested on data from the 2018 Oaxaca earthquake in Mexico from Sentinel-1. This work is a step towards using neural networks for a fine-granular tile-based validation of interferograms and automatically removing unwanted effects from InSAR signals, as well as towards enhancing the agility of disaster response programs. The open source code is available in the PyInSAR package on GitHub under the MIT license. We acknowledge support from NASA AIST80NSSC17K0125 (PI Pankratius) and NSF ACI1442997 (PI Pankratius).

GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM.

Phase unwrapping (PU) is a key processing step in interferometric synthetic aperture radar (InSAR). To date, a number of skillful single-baseline (SB) and multibaseline (MB) PU methods exhibiting different advantages have been proposed. However, as the basic principles of SB and MB PUs are essentially different, it is difficult to effectively and systematically compare the performance of SB and MB PUs, despite the knowledge that this type of analysis is important for allowing the ever-increasing number of InSAR practitioners to choose a suitable approach for practical applications and to optimally plan future InSAR satellite missions. Recently, the framework of the two-stage programming approach (TSPA) was proposed, and this allows for the majority of the existing SB PU methods to be transplanted into the MB domain, to allow practitioners to feasibly and comprehensively compare SB and MB PU methodologies. In this study, the digital elevation model (DEM) reconstruction accuracy is compared between the classical SB PU methods and their corresponding TSPA-framework-based MB PU methods using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L^{p}$ </tex-math></inline-formula> -norm model. Interestingly, we observed that although the number of PU residues in the MB case is larger than that in the SB case, the MB PU performance is better. The reason for this is that the type of MB residue is typically dipole, so the average length of the required MB branch-cut is shorter than that of SB. It is also demonstrated that the TSPA framework can effectively improve the PU accuracy of many existing SB PU methods when the number of input interferograms is sufficient.

In a typical interferometric synthetic aperture radar (IFSAR) system employed for terrain elevation mapping, terrain height is estimated from phase difference data obtained from two phase centers separated spatially in the cross-track direction. In this paper we show how the judicious design of a three phase center IFSAR renders phase unwrapping, i.e., the process of estimating true continuous phases from principal values of phase, a much simpler process that inherent in traditional algorithms. With three phase centers, one IFSAR baseline fan be chosen to be relatively small so that all of the scene's terrain relief causes less than one cycle of phase difference. This allows computation of a coarse height map without use of any form of phase unwrapping. The cycle number ambiguities in the phase data derived from the other baseline, chosen to be relatively large, can then be resolved by reference to the heights computed from the small baseline data. This basic concept of combining phase data from one small and one large baseline to accomplish phase unwrapping has been previously employed in other interferometric problems. The new algorithm is shown to possess a certain form of immunity to corrupted interferometric phase data that is not inherent in traditional 2D path-following phase unwrappers. This is because path-following algorithms must estimate, either implicitly or explicitly, those portions of the IFSAR fringe data where discontinuities in phase occur. Such discontinuities typically arise form noisy phase measurements derived from low radar return areas of the SAR imagery. When wrong estimates are made as to where these phase discontinuities occur, errors in the unwrapped phase values can appear due to the resulting erroneous unwrapping paths. This implies that entire regions of the scene can be reconstructed with incorrect terrain heights. By contrast, since the new method estimates the continuous phase at each point in the image by a straightforward combination of only the measured phases from the small and large baseline, phase estimation errors are confined to that point. We derive quantitative expressions for the new algorithm that relate the probability of selecting the wrong phase cycle to parameters of the interferometer. We then demonstrate that use of median filtering can very effectively mitigate those cycle errors that do occur. By use of computer simulations, we show how the new algorithm is used to robustly construct terrain elevation maps.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Phase unwrapping (PU) is one of the key processing step in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from the observation of its noisy principal (wrapped) values. This paper presents a 2D InSAR phase unwrapping algorithm exploiting the capability of the Kalman algorithm to simultaneously perform noise filtering and phase unwrapping. The performance of the proposed algorithm is tested on simulated InSAR images of the Gaussian type proving the effectiveness of our method.

Interferometric Synthetic Aperture Radar (InSAR) applies the interferograms of two or more SAR images to generate maps of surface deformation or digital elevation models. InSAR operational processing chain for displacement map generation comprises of five major stages: co-registration and re-sampling, interferogram generation, flat-earth correction, topography correction and phase unwrapping. This paper discusses and evaluates the authors' proposed algorithms for SAR image matching and for topographic and reference-phase corrections, which improve conventional InSAR processing techniques in terms of increasing efficiency and reducing the time and computational effort. Our proposed algorithms are implemented in MATLAB and evaluated with respect to the conventional InSAR processing performed by DORIS software for a pair of Envisat ASAR data associated with the 2003 BAM earthquake.

The FMCW radar has lots of advantages such as compact, energy effective and low cost. It is suitable for mounting on the vehicle and also on small sized UAV. In this paper, the high squint angle effect is considered for both the FMCW synthetic aperture radar (SAR) and the inverse synthetic aperture radar (ISAR) in the case of the high resolution vehicle radar. The 2nd range compression is performed for removing the range and the azimuth coupling effect under the high squint angle situation. The polar reformatting is performed for improving the radar resolution in the ISAR. By keeping practical situation in mind, the image qualities under the squint angle with random error is investigated. In addition, the simulation software for the SAR and the ISAR are developed.

Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method.

As is well known, multibaseline phase unwrapping (PU) is put forward to overcome single-baseline PU in discontinuous-terrain-height estimation. This letter presents a refined algorithm based on the cluster analysis (CA)-based noise-robust efficient multibaseline PU algorithm proposed by H. Yu. The basic idea is to combine multiple interferometric synthetic aperture radar interferograms with different baseline lengths by a linear combination. The new interferograms after the linear combination increase the ambiguity heights. The number of resulting groups on the envelope of the intercept histogram is decreased and the distance between different intercept groups is widened. Compared with the conventional CA method, the significant advantage of the refined CA (RCA) algorithm is that it improves noise robustness when the intercept groups are densely distributed. The proposed RCA algorithm is validated using the simulated interferometric data. The results demonstrate that the noise robustness performance is better than that of the CA method.

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.