BM3D Denoising for a Cluster-Analysis-Based Multibaseline InSAR Phase-Unwrapping Method

This paper introduces new polarimetric algorithms for generating 3-D images and estimating scattering mechanisms from polarimetric multibaseline (MB) interferometric synthetic aperture radar (SAR) measurements. First, an MB interferometric SAR signal model is generalized to the fully polarimetric configuration, establishing the notion of polarimetric reflectivity. Subsequently, polarimetric beamforming, Capon, and MUSIC methods that determine optimal polarization combinations for height estimation are developed. These new techniques allow for extracting the height of reflectors, the associated scattering mechanisms, and the polarimetric (pseudo)reflectivities. By means of polarimetric dual-baseline interferometric SAR observations of an urban environment, the performance of the conceived algorithms is examined in detail. Producing 3-D images of a building layover, the quality of the approaches is compared in terms of refined resolution and lowered side lobes. Furthermore, the scattering processes occurring in urban scenes are investigated thoroughly by analyzing the optimal reflection types. The algorithms are validated using dual-baseline polarimetric SAR interferometric data at L-band acquired by German Aerospace Center's experimental SAR system over Dresden city.

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.

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.

For the multibaseline (MB) synthetic aperture radar (SAR) interferometry (InSAR), MB phase unwrapping (PU) is an important step. With the rapid development of MB InSAR, the size of the datasets from the MB InSAR system is becoming increasingly larger. Under the situation of bigdata, MB PU may face new problems with insufficient computing resources, or take too much running time to get the PU result. In order to deal with such case, we propose a convex hull and cluster-analysis based fast large-scale MB PU method (CCFLS) with enlightened by the single baseline (SB) PU method (CHFLS) from H. Yu [1]. CCFLS uses the clustering phenomenon of the MB residues to generate the convex hull of residues set with balance polarity, and avoids spending the computation resources on the area within the convex hull, so that the high-precision PU solution can be quickly obtained. The theoretical analysis and experiment results indicate that CCFLS can effectively reduce memory consumption and calculation time.

Multibaseline (MB) synthetic aperture radar inter-ferometry (InSAR) is capable of reconstructing steep terrain height profiles, and MB phase unwrapping (PU) is one of the most critical and challenging steps in its processing chain. Existing MB PU algorithms usually suffer from poor noise robustness or low computational efficiency. In this work, we propose a fast and robust MB PU algorithm, including three main steps: 1) construct the triangulation network guided by the quality map; 2) obtain robust estimation of ambiguity number gradients based on the intrinsic relationship between interferograms and gradient regularization; 3) solve minimum cost flow problem with modified weights. The mean absolute errors of the height profiles constructed by the proposed method are 1.6m and 1.3m for simulated data and real data experiments respectively, verifying the effectiveness of the proposed method.

A fast multi-baseline and multi-frequency band phase-unwrapping algorithm

Multi-baseline interferometric synthetic aperture radar (InSAR) techniques have been accepted as effective remote sensing tools for detecting and monitoring landslide movements. With the use of stacked synthetic aperture radar (SAR) imageries, it is capable of generating precise ground displacement time-series. In order to further suppress noise induced by atmospheric effects, a post-process step, named as temporal filter, is required to be applied to the final displacement time-series in most applications. As displacement signals are strongly correlated in time, the traditional window-based/least squares filter is widely adopted. Since the window-based filter balances a tradeoff between noise smoothing and signal smoothing, the resulting time-series may strongly deviate from the true values when ground displacements appear high nonlinearity. In this paper, a new approach is proposed to reconstruct the InSAR deformation time-series for rainfall-induced landslides. This method establishes a nonparametric model based on the idea of Gaussian process regression (GPR) and introduces precipitation data as a priori knowledge. A strong relationship between rainfall history and ground movements is therefore constructed, which is extremely helpful in preventing the loss of high-frequency displacement signals. The proposed approach was applied to the InSAR landslide displacement time-series obtained from 108 European Space Agency (ESA) Sentinel-1A satellite SAR images. Experimental results demonstrate that it is capable of preserving the details of the temporal evolution of ground displacements effectively compared to the traditional window-based method, in particular on the surface of sliding mass.

For synthetic aperture radar (SAR) interferometry (InSAR), phase unwrapping (PU) is an important and difficult step. Due to the high computational complexities of the classical and skilled PU methods, the size and number of interferograms to be processed together must be considered in InSAR applications. However, with the rapid development of InSAR technology, the scale of interferometric data has significantly increased, which has brought two difficulties to InSAR processing: 1) excessive memory consumption and 2) unacceptably long computing time. To solve these problems, a fast large-scale PU method based on cluster analysis and convex hull (CCFLS) is proposed in this paper. It was developed for single-baseline (SB) InSAR and refined under the multibaseline (MB) two-stage programming approach InSAR (TSPA-InSAR) framework. The main idea of CCFLS is to reduce the consumption of computing resources by discarding the PU processing of worthless low quality areas. The key to this work is to determine the discarded region at a low computational cost while ensuring the same PU accuracy for the remaining region as global processing. The theoretical analysis demonstrates the advantage of the CCFLS method for large-scale InSAR data processing, and experiments also verify that CCFLS can greatly reduce the consumption of computing resources while ensuring the PU accuracy of the solved region.

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.

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.

In current Interferometric synthetic aperture radar (InSAR) applications, small baseline subsets (SBAS) technique is frequently applied by setting both temporal baseline and perpendicular baseline thresholds to select interferograms with less decorrelations. However, in real cases, some high quality interferograms may be discarded and some low quality interferograms may be included based on the former thresholds. On the other hand, with the emergence of Big SAR data era, it is time-consuming and bothersome to determine real high quality interferograms manually or visually. Therefore, this paper proposes a means to determine high quality interferograms based on two thresholds, the one is the coherence of filtered interferograms, and the other is the percentage of high coherence points in interferograms. To this ends, first, robust estimation of coherence matrix of multi-baseline InSAR stack is adopted to restrain some heterogeneous points. Second, coherence matrix for each pixel is decomposed to get dominant phase signal based on the SAR scatterer mechanism. Once the interferograms are determined, phase unwrapping is easily conducted based on the high quality coherent points. Real eight scenes TerraSAR-X data over one land subsidence region is applied to verify of the proposed 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.

Phase unwrapping is an essential process in synthetic aperture radar interferometry that restores phase signals constrained within the range of (-π, π) to their true phase values. Traditional algorithm-based methods can introduce significant errors due to rapid and steep phase gradient or noise, which negatively impact terrain elevation and surface displacement analyses. To overcome these limitations, deep learningbased phase unwrapping techniques have been proposed; however, there has been insufficient previous studies due to the lack of accurate training and test data. This paper aims to share the training data generated using the phase unwrapping simulation method with locally-different phase noise. The data were generated by the simulation of topographic phases and phase noise, atmospheric and orbital distortions. Additionally, data augmentation for phase variation and noise levels was applied to address data imbalance issues. The shared data consists of two types: one with a constant phase noise level for each patch, and another that simulates locally different phase noise based on augmented coherence data. This data is primarily effective for unwrapping topographic phase components and holds significance as the first phase unwrapping training data of synthetic aperture radar interferograms shared in Korea. We expect this resource to serve as foundational data for future phase unwrapping technology research, including applications for upcoming satellites like KOMPSAT-6 and water resource satellites.

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.