Multi-baseline Interferometric Synthetic Aperture Radar Research Articles

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

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, the cluster analysis (CA)-based MBPU method often suffers from the problem of low noise robustness. Therefore, the block-matching and 3D filtering (BM3D) algorithm, one of the most effective filtering methods for image denoising, is applied to improve the performance of the method. Five different filtering strategies for applying BM3D are proposed in the paper: interferogram filtering (IFF), intercept filtering (ICF), cluster number filtering (CNF), unwrapped phase filtering (UPF), and simultaneous filtering (STF). In particular, while keeping the general structure of BM3D, four different similarity measures are defined for interferograms, intercepts, clusters, and unwrapped phases to accommodate the special characteristics of different filtering objects. Experiments on synthesized and real InSAR datasets prove their feasibility and effectiveness, and the experiment results show that (1) the PU accuracy and robustness of the CA-based MBPU method can be greatly improved by adding BM3D denoising; (2) simultaneous filtering of interferograms, intercepts, cluster numbers, and unwrapped phases works best, but with the worst time complexity; (3) when filtering is performed for only one object of the CA-based MBPU method, the filtering effect of CNF and UPF is better than that of IFF and ICF; and (4), considering the three indicators of PUSR, NRSE, and time consumption, CNF and UPF should be the best choices.

An Anti-2D Deceptive Jamming Method for Multi-Baseline Interferometric SAR Based on Co-Localization Jammer

Aimed at the problem that range-azimuth joint jamming can effectively generate high fidelity false targets in synthetic aperture radar (SAR) images, an anti-2D deceptive jamming method for multi-baseline interferometric SAR is proposed in this paper. The method consists of three steps: identification, classification and suppression. Firstly, the target is detected in the sea clutter background using constant false alarm rate algorithm, and then the false target is identified base on the difference of the spatial position of the target in different SAR images. Secondly, through the analysis of the generating principle of deceptive jamming, the false target is classified by multi-baseline interferometric SAR according to partial prior knowledge, which is designed based on the differences between the interferometric phase of the true and false target. Finally, the position of the active jammer is co-located underlying the phase equalization characteristics between multiple baselines and the geometric configuration of the SAR, and the adaptive beamforming method is supplied to suppress the jamming signal. The proposed method has a significantly improved ability to suppress jamming relative to interferometric phase cancellation. The effectiveness of the proposed method is verified by simulation.

Improved DEM Reconstruction Method Based on Multibaseline InSAR

Digital elevation models (DEMs) are vital in the geosciences and many other fields. Interferometric synthetic aperture radar (InSAR), an advanced earth observation technology, has shown its potential in DEM reconstruction. Multi baseline InSAR (MB-InSAR) is currently improving the precision of DEM reconstruction by combining multiple interferograms. However, MB-InSAR for DEM generation can result in severe decorrelation, which may cause significant gaps in the final DEM product. To solve this problem, an improved MB-InSAR DEM reconstruction method is proposed in this study, which we term as the dynamic DEM calculation algorithm. The proposed method can estimate the DEM pixel-by-pixel, which allowed us to select the interferograms dynamically and therefore minimize the void values. For the performance test of the proposed method, 25 ascending and 20 descending TerraSAR-X images over Heifangtai (China) were collected to form repeat-pass interferograms and produce the DEM using the proposed method. Results showed that the number of valid pixels increased by approximately 20% compared with the traditional MB-InSAR DEM reconstruction method without loss of precision, thereby illustrating the feasibility of the proposed method.

Mapping Complete Three-Dimensional Ice Velocities by Integrating Multi-Baseline and Multi-Aperture InSAR Measurements: A Case Study of the Grove Mountains Area, East Antarctic

The Antarctic is one of the most sensitive areas to climate change, and ice velocity is a fundamental parameter for quantitatively assessing the glacier mass balance. Interferometric synthetic aperture radar (InSAR), a powerful tool for monitoring surface deformation with the advantages of having high precision and wide coverage, has been widely used in determining ice velocity in the Antarctic. However, the mapping of complete three-dimensional (3D) ice velocities is greatly limited by the imaging geometries and digital elevation model (DEM)-induced errors. In this study, we propose the integration of multibaseline and multiaperture InSAR measurements from the ENVISAT ASAR datasets to derive complete 3D ice velocities in the Grove Mountains area of the Antarctic. The results show that the estimated complete 3D ice velocities are in good agreement with MEaSUREs and GPS observations. Compared with the conventional 2D and quasi-3D ice velocities, the complete 3D ice velocities can effectively eliminate the effects of DEM errors and elevation changes and are also capable of retrieving the thickness change of the ice, which provides important information on the origin of mass transition.

Multibaseline InSAR Layover Detection Based on Local Frequency and Eigenvalue

Layover detection has long been the focus of attention for interferometric synthetic aperture radar (InSAR) data processing. As for the existing layover detection methods, most of them are applied to single-baseline scenarios. Since multibaseline InSAR provides more image samples, the performance of layover detection can be further improved. In this article, a joint detection method of layover is proposed on the basis of local frequency and eigenvalue of multibaseline InSAR. To obtain a precise local frequency, maximum likelihood estimation is performed for flattened interferometric phase fusion and removing the reference terrain phase is chosen to ensure the proportionality of flattened phase to the baseline length. Adaptive image selection based on coherence is proposed to obtain a distinct eigenvalue. Then, the joint detection method is elaborated on. Based on the results of local frequency estimation and eigenvalue decomposition, precise thresholds are set for joint detection, and joint judgment is made by combining the layover detected by local frequency, the layover detected by eigenvalue, and the thresholds. Finally, the proposed method is validated through comparative experiments on simulated data and real data. Both theoretical analysis and experimental results show the feasibility and superiority of the proposed method.

SAR Tomography at the Limit: Building Height Reconstruction Using Only 3–5 TanDEM-X Bistatic Interferograms

Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas. In order to obtain a plausible reconstruction, it is necessary to use more than twenty interferograms. Hence, these methods are commonly not appropriate for large-scale 3-D urban mapping using TanDEM-X data where only a few acquisitions are available in average for each city. This work proposes a new SAR tomographic processing framework to work with those extremely small stacks, which integrates the non-local filtering into SAR tomography inversion. The applicability of the algorithm is demonstrated using a TanDEM-X multi-baseline stack with 5 bistatic interferograms over the whole city of Munich, Germany. Systematic comparison of our result with TanDEM-X raw digital elevation models (DEM) and airborne LiDAR data shows that the relative height accuracy of two third buildings is within two meters, which outperforms the TanDEM-X raw DEM. The promising performance of the proposed algorithm paved the first step towards high quality large-scale 3-D urban mapping.

Performance Analysis and Optimization of Spaceborne Multi-baseline InSAR Systems

The spaceborne interferometric synthetic aperture radar (InSAR) technique has become a useful tool to obtain the surface elevation or deformation. The multi-baseline InSAR technique is capable of fusing data from multiple channels for interferometric processing and improving the accuracy of elevation estimation of the observed scenes. In this paper, we study the joint distribution to statistically characterize the interferometric phases of multi-baseline InSAR. The baseline parameters are sought based on the second-order moment of the interferometric phase joint probability density function (PDF). The study done in this thesis provides a basis for the design of parameters for multi-baseline interferometric systems.

Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach

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.

Multibaseline Interferometric Phase Denoising Based On Kurtosis In the NSST Domain.

Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.

Refined Two-Stage Programming Approach of Phase Unwrapping for Multi-Baseline SAR Interferograms Using the Unscented Kalman Filter

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

Unwrapped Phase Estimation via Normalized Probability Density Function for Multibaseline InSAR

Interferometric synthetic aperture radar (InSAR) is a powerful technique for obtaining terrain information based on the interferometric phase. Multibaseline (MB) InSAR is an extension of the conventional InSAR and is used to improve the estimation accuracy and reliability of the unwrapped phase. Based on a newly defined normalized phase probability density function (pdf), a novel wrapped-to-unwrapped phase (W2UP) estimation method is proposed for MB-InSAR. First, the concept of the normalized pdf is introduced to overcome the limitation of the fixed $2\pi $ period for different baseline cases. Then, a new maximum likelihood estimation is established using the MB normalized pdfs, which has a much steeper peak around the true phase value than the single baseline case and leads to higher estimation accuracy. The proposed W2UP method estimates the unwrapped phase from multiple filtered interferograms, so it is less influenced by the phase noise. Both the theoretical analysis and results using the simulated and real MB data are provided to verify the effectiveness of the proposed method.

Multibaseline TanDEM-X Mangrove Height Estimation: The Selection of the Vertical Wavenumber

We generated a large-scale mangrove forest height map using multiple TanDEM-X (TDX) interferometric synthetic aperture radar (InSAR) acquisitions with various spatial baselines in order to improve the height estimation accuracy across a wide range of forest heights. The forest height inversion using InSAR data is strongly dependent upon the vertical wavenumber (i.e., perpendicular baseline). First, we investigated the role of the vertical wavenumber in forest height inversion from InSAR data using the sensitivity of the interferometric (volume) coherence to forest height. We used corrected but lower resolution and accuracy Shuttle Radar Topography Mission (SRTM) mangrove height maps as a priori information over Akanda and Pongara National Parks in Gabon to estimate lower and upper boundaries of the vertical wavenumber over test sites from the measured coherence-to-height sensitivity. Only TDX acquisitions within the boundaries of the vertical wavenumber were selected and combined for multibaseline mangrove height inversion. Mangrove forest height was obtained with multibaseline TDX acquisitions and was validated against the reference height derived from field measurement data providing improvements in multibaseline inversion over existing height estimates (i.e., SRTM height) and single-baseline inversions (multibaseline inversion: $r^{2}= {\text{0.98}}$ , root mean square error (RMSE) of 2.73 m; SRTM height: $r^{2}= {\text{0.86}}$ , ${\text{RMSE}}= {\text{7.21}}$ m; single-baseline inversions: $r^{2}= {\text{0.08}}{\hbox{-}} {\text{0.97}}$ , ${\text{RMSE}}= {\text{3.86}}$ –11.10 m). As a result, to accurately estimate forest heights over a wide range (3–60 m), multibaseline InSAR acquisitions (at least three different baselines) are needed to exclude biases associated with the vertical wavenumber in forest height inversion.

An improved SAR interferogram denoising method based on principal component analysis and the Goldstein filter

ABSTRACTInterferogram filtering is an important data processing step in Interferometric synthetic aperture radar (InSAR) applications, which has a direct impact on the accuracy of the phase unwrapping and digital elevation model (DEM) or deformation results retrieval. An improved synthetic aperture radar (SAR) interferogram denoising method based on principal component analysis and the Goldstein filter is proposed, which can improve the coherence of interferogram remarkably and get more coherent targets. First, homogeneous pixels are identified with stacks of SAR amplitude data, which can obtain the unbiased coherence estimation. Then, the noise phase of one resolution unit is suppressed based on the principal component analysis of multi-baseline InSAR coherence stacks by considering the relationship between pixel size and scattering mechanism. Finally, the remaining noise is smoothed with the iterative Goldstein filter over spatial domain. The proposed method is tested over one deformed and low-coherence region to verify the better performance in the terms of noise reduction and coherence increase.

Multi-baseline SAR Interferometry using Elaboration of Amplitude and Phase Data

Multi-baseline interferometric Synthetic Aperture Radar (In-SAR) systems can be exploited to estimate the Digital Elevation Model (DEM) of the observed scene without ambiguities and with an increased accuracy, even in the case of high sloped ground regions. The techniques usually used exploit only the interferometric phase information and they are based on Maximum Likelihood (ML) estimation. An important problem to be taken into account is the mutual correlation of the (complex) interferometric images which impedes the closed form evaluation of the interferometric phases likelihood function. Moreover the statistical independence approximation of the phase interferograms is usually adopted. In this paper we present a method exploiting both amplitude and phase of the interferometric images, with the purpose of expressing the multi-baseline likelihood function in closed form, and we show that, when the number of baselines increases, to achieve an higher estimation accuracy the images mutual correlation cannot be neglected. We also show that to obtain a full resolution speckle reduced intensity image from several full resolution multi-baseline interferometric (complex) images, a phase compensation and a whitening operation have to be performed before averaging the data intensities.

Adaptive Covariance Matrix Estimation for Multi-Baseline InSAR Data Stacks

For many multidimensional applications of synthetic aperture radar (SAR) imaging, the estimation of the covariance matrix for each resolution cell is a critical processing step. The context of this work is the application of covariance matrix estimation for multi-baseline interferometric SAR data sets. In order to ensure local stationarity, which is needed for an unbiased estimation, adaptive techniques are necessary. In this paper, a new approach for adaptive covariance matrix estimation is proposed and evaluated based on measures known from the field of image processing. The procedure is centered around the idea of checking whether the neighboring pixels belong to the same statistical distribution as the currently investigated pixel by applying a threshold to the respective probability density function. All inlier pixels are then used to estimate the complex covariance matrix of the reference pixel. From this covariance matrix, both amplitude and interferometric phase values are extracted, which are then combined for all pixels in the stack in order to employ techniques for the evaluation of filtering efficiency that are typically used in image denoising research. It is found that the proposed algorithm provides high filtering efficiency and good detail preservation at the same time. Apart from that, it is found to be particularly suitable for small-sized stacks of coregistered SAR imagery.

Multibaseline SAR Interferometry from Complex Data

Multibaseline interferometric synthetic aperture radar (In-SAR) systems are used to obtain the height profile of the observed ground scene with high accuracy. The techniques that are commonly used exploit only the interferometric phase information, and are based on maximum likelihood (ML) estimation. Due to the difficulty of expressing the multi-baseline likelihood function in closed form, they adopt the statistical independence approximation of the interferometric phases. In this paper, we investigate the effect of this approximation, and analyze the performance of two statistical methods exploiting complex SAR images, which takes into account the mutual correlation among all the interferometric images. These two methods are based on a local and a contextual approach. The local one is based on an ML estimator, and processes each pixel independently from the neighboring ones. The contextual approach adopts a Bayesian estimator in order to regularize and improve the height reconstruction. The presented results show that the inclusion of SAR image correlation information allows to improve the height reconstruction accuracy. The proposed method also provides an estimation of speckle-reduced image intensity.

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

Multi-baseline and multi-frequency band can improve the performance of phase unwrapping. This paper, taking the multi-baseline InSAR (interferometric synthetic aperture radar) system as an example, proposes a fast method for multiple-baseline and multi-frequency band phase unwrapping in the frequency domain. The basic idea is to perform a least-squares minimization of the differences between the estimated and absolute phase gradients on all interferograms simultaneously, which are all in the frequency domain. Results on real and simulated data show that the frequency-domain method yields results similar to those of the time-domain method while improving efficiency by avoiding mirroring operations.

Morphological Database of Paris for Atmospheric Modeling Purposes

This paper presents a morphological database, aimed to be applied as boundary data for atmospheric modeling. The database has been created from freely available sources and 13 ASAR images that were provided by European Space Agency (ESA) through Announcement of Opportunity (AO). Urban themes were extracted from optical data and digital maps by supervised spectral minimum distance to means classification. Urban topography was modeled using multibaseline Interferometric Synthetic Aperture Radar (InSAR) with phase unwrapping based on Maximum Likelihood (ML) estimation. In combining obtained InSAR height data with urban classification from optical images, additional features that are beyond the SAR resolution were added to the urban topography layer. The methods of retrieval of morphological data are discussed as well as the generation of the height model. The results are compared to the existing Shuttle Radar Topography Mission (SRTM) elevation model and the BDTopo Parisian database. We also address an example application of the database, a Large Eddy Simulation (LES) of urban canopy flow over the Paris area.

Three-Dimensional Imaging and Scattering Mechanism Estimation Over Urban Scenes Using Dual-Baseline Polarimetric InSAR Observations at L-Band

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.

Vegetation profiles in tropical forests from multibaseline interferometric synthetic aperture radar, field, and lidar measurements

This paper addresses the estimation of vertical vegetation density profiles from multibaseline interferometric synthetic aperture radar (InSAR) data from the AirSAR aircraft at C band over primary, secondary, and abandoned‐pasture stands at La Selva Biological Station, Costa Rica in 2004. Profiles were also estimated from field data taken in 2006 and lidar data taken with the LVIS, 25 m spot instrument in 2005. After motivating the study of tropical forest profiles based on their role in the global carbon cycle, ecosystem state, and biodiversity, this paper describes the InSAR, field, and lidar data acquisitions and analyses. Beyond qualitative agreement between profiles from the 3 measurement techniques, results show that InSAR and lidar profile‐averaged mean height have RMS scatters about field‐measured means of 3.4 m and 3.2 m, 16% and 15% of the average mean height, respectively. InSAR and lidar standard deviations of the vegetation distribution have RMS scatters about the field standard deviations of 1.9 m and 1.5 m, or 27% and 21%, respectively. Dominant errors in the profile‐averaged mean height for each measurement technique were modeled. InSAR inaccuracies, dominated by ambiguities in finding the ground altitude and coherence calibration, together account for about 3 m of InSAR error in the mean height. The dominant, modeled error for the field measurements was the inaccuracy in modeling the trees as uniformly filled volumes of leaf area, inducing field errors in mean height of about 3 m. The dominant, modeled lidar error, also due to finding the ground, was 2 m.