Interferometric Parameters Research Articles

Interferometric Calibration Model for the LuTan-1 Mission: Enhancing Digital Elevation Model Accuracy

The LuTan-1 (LT-1) mission, China’s first civilian bistatic spaceborne Synthetic Aperture Radar (SAR) mission, comprises two L-band SAR satellites. These satellites operate in bistatic InSAR strip map mode, maintaining a formation flight with an adjustable baseline to generate global digital elevation models (DEMs) with high accuracy and spatial resolution. This research introduces a dedicated interferometric calibration model for LT-1, tackling the unique challenges of the bistatic system, such as interferometric parameter coupling and the π-ambiguity problem caused by synchronization phase errors. This study validates the model using SAR images from LT-1 and Xinjiang corner reflector data, achieving interferometric phase accuracy better than 0.1 rad and baseline accuracy better than 2 mm, thereby producing high-precision DEMs with a height accuracy meeting the 5 m requirement.

All-Optical format conversion from PAM4 to QPSK based on non-degenerate Phase-Sensitive amplification and pump assisted nonlinear optical loop mirror

An all-optical format conversion (AOFC) scheme from 4-ary pulse amplitude modulation (PAM4) to quadrature phase shift keying (QPSK) based on the non-degenerate phase-sensitive amplification (PSA) and the pump assisted nonlinear optical loop mirror (PA-NOLM) is proposed and numerically simulated for the first time. The input unipolar PAM4 signal with equal amplitude distance (UPAM4-A) is coupled with two input pump lights and injected into the symmetric interferometric fiber optic parameter amplifier (FOPA) to achieve the separation of the UPAM4-A and the new pump light generated at the signal frequency based on the non-degenerate PSA. When the filtered out UPAM4-A is coherent added with the new generated pump, the UPAM4-A signal to the biplolar PAM4 (BPAM4) signal. When the converted BPAM4 is sent into the PA-NOLM after adjusting its power, the BPAM4 signal could be converted into the QPSK signal. The error vector magnitude (EVM) and bit error ratio (BER) performance of the system are measured before and after AOFC with different input and receiver optical signal to noise ratio (OSNR). For the input UPAM4-A signal with an input OSNR of 27 dB, there is about 8.2 dB BER performance improvement in receiver OSNR for the final converted QPSK signal. Additionally, the proposed non-degenerate PSA configuration can be extended to realize the AOFC from one QPSK to two on–off keying (OOK) signals. The scheme can be applied in the optical gateway connecting the long-haul and the short-reach optical networks with suitable modulation formats.

Interferometric Calibration Based on a Constrained Evolutionary Algorithm without Ground Control Points for a Tiangong-2 Interferometric Imaging Radar Altimeter

The interferometric imaging radar altimeter (InIRA), mounted on the Tiangong-2 space laboratory, utilizes a small incidence and a short interferometric baseline to achieve altimetry for wide swathes of ocean surface topography and inland water surface elevation. To obtain a high-precision digital elevation model (DEM), calibration of the interferometric system parameters is necessary. Because InIRA utilizes the small-incidence interference system design, serious coupling occurs between the interferometric parameters. Commonly used interferometric calibration methods tend to fall into the local optimal solution for InIRA. Because evolutionary algorithms have a stronger robustness and global search ability, they are better suited to handling the solution space structure under the coupling of complex interferometric parameters. This article establishes an interferometric calibration optimization model for InIRA by utilizing the relative flatness of the lake surface as an inequality constraint. Furthermore, an adaptive penalty coefficient constraint evolutionary algorithm is designed to solve the model. The proposed method was tested on actual InIRA data, and the results indicate that it efficiently adjusts interferometric parameters, enhancing the precision of measurements for Qinghai Lake elevation.

Block Adjustment of Orientation Parameters and Interferometric Parameters Using Very High-Resolution SAR Data for DSM Mapping

Block Adjustment of Orientation Parameters and Interferometric Parameters Using Very High-Resolution SAR Data for DSM Mapping

An Improved Independent Parameter Decomposition Method for Gaofen-3 Surveying and Mapping Calibration

The Gaofen-3 (GF-3) satellite can provide digital elevation model (DEM) data from its interferogram outputs. However, the accuracy of these data cannot be ensured without applying a surveying and mapping (SAM) calibration process, thus necessitating geometric and interferometric calibration technologies. In this paper, we propose an independent parameter decomposition (IPD) method to conduct SAM calibration on GF-3 data and generate high-accuracy DEMs. We resolved the geometric parameters to improve the location accuracy and resolved the interferometric parameters to improve the height accuracy. First, we established a geometric calibration model, analyzed the Range–Doppler (RD) model and resolved the initial imaging time error as well as the initial slant range error. Then, we established a three-dimensional reconstruction (TDR) model to analyze the height error sources. Finally, the interferometric phase error and baseline vector error were precisely estimated to ensure the vertical accuracy of the interferometric results by establishing the interferometric calibration model. We then used the GF-3 interferometric data derived on the same orbit in a north–south distribution to conduct the calibration experiment. The results show that the plane positioning accuracy was 5.09 m following geometric calibration, that the vertical accuracy of the interferometric results was 4.18 m following interferometric calibration and that the average absolute elevation accuracy of the derived DEM product was better than 3.09 m when using the GF-3 SAR data, thus confirming the correctness and effectiveness of the proposed GF-3 IPD calibration method. These results provide a technical basis for SAM calibration using GF-3 interferograms at the 1:50,000 scale in China.

Разработка математической модели интерферометрической системы определения доплеровского сдвига частоты.

This work presents the result of the received signal measuring system for processing Doppler frequency theoretical research. In the researching device means of controlling laser frequency whose amplitude is proportional to the Doppler frequency shift of the received RF signal is realized. The coherent laser beam is divided in two forming interference pattern. In this case, one of the beams passes through the delay line, which leads to a phase shift in the optical wave. The rate of this phase shift is proportional to the laser frequency, changes in which cause corresponding changes in the interference pattern. Changes in the interference pattern in center analysis makes it possible to determine the changes of the laser frequency, which depends on Doppler frequency shift of the received RF signal. In this work opposition of the requirements for the Doppler frequency shift determination interferometric system parameters (the coefficient of proportionality to conversion Doppler frequency shift of the received RF signal in laser frequency and time delay) is discovered: large dynamic measurement range and high Doppler frequency shift measurement resolution. The processing the received RF signal method is proposed. This method take into consideration these requirements. The proposed measurement algorithm implements the multiscale principle. It is pointed that the proposed processing the measurements results method can be implemented both the parallel processing in channels with different values of conversion coefficients, and sequential – with their iterative change.

Dynamical evaluation of interference fringe parameters by the Wiener adaptive filtering method

Application of the Wiener adaptive filtering method for estimation of interferometric signal parameters is presented and analyzed. It is shown that the fringe phase, as well as the envelope of low-coherence interference fringes, can be evaluated directly from the adaptive filter impulse response obtained under the criterion of the minimum error variance of a processed fringe signal and predefined reference signal. The method does not limit the number and value of the phase shifts and allows determination of the actual phase step. The accuracy of the method is evaluated and experimental results of dynamical fringe processing in low-coherence interferometry are presented.

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named Φ-Net, for the joint estimation of the interferometric phase and coherence. Φ-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the Φ-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that Φ-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dual-antenna InSAR data processing are performed to verify the effectiveness of this method.

Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

In this article, five-component model-based decomposition methods of polarimetric synthetic aperture radar (PolSAR) (P5SD) and polarimetric synthetic aperture radar interferometry (PolInSAR) (PI5SD) using coupling scattering mechanisms are proposed. The target of these methods is to overcome the overestimation of volume scattering and to mitigate the mixed ambiguity of the scattering mechanism. First, coupling scattering models are proposed to decompose the coherency matrix into five submatrices. According to the traditional seven-component decomposition method proposed by Singh et al. , the helix and mixed dipole can be merged into a coupling component, while the oriented dipole and compound dipole can also be generated in a general form. Then, the refined volume scattering models of PolSAR are introduced to characterize the volume scattering power of various land covers. The polarimetric interferometric similarity parameter (PISP) is introduced to modify the refined volume scattering models, and it can be used to further reduce the volume scattering contribution of building areas with large orientation angles. In addition, a feature named $\theta _{dv}$ , total power, and PISP are embedded in the building area identification, and the building extraction results are used as the branch condition of the volume scattering model selection. In this article, the validity of P5SD and PI5SD can be demonstrated using airborne $L$ -band E-SAR PolInSAR datasets and airborne $C$ -band PolInSAR data collected by the Aerospace Information Research Institute, Chinese Academy of Sciences. The experimental results demonstrate that the P5SD and PI5SD can be used to effectively interpret the scattering characteristics of the ambiguous regions.

Monthly Deforestation Monitoring with Sentinel-1 Multi-temporal Signatures and InSAR Coherences

<p class="p1">Sentinel-1 interferometric time-series allow for the accurate retrieval of the target’s temporal decorrelation and, therefore, the inversion of land cover information and its temporal monitoring. This paper describes the development of an observation scenario for monitoring monthly deforestation over the Amazon rainforest, which relies on the use of radar for overcoming the physical limitations of optical sensors caused by the presence of cloud coverage. Specifically, we implement a classification scheme that exploits multi-temporal SAR features, such as backscatter, spatial textures, and interferometric parameters, to map forested areas. Distinct forest maps are generated for consecutive months and further processed to detect deforestation phenomena and map clear-cuts evolution. The obtained results are validated by selecting cloud-free Sentinel-2 multispectral data on the selected area and acquired during the same observation time.</p>

Interference of two diverging beams reflected from a plane parallel plate: stationary fringes with characteristic stable period.

We investigated interference fringes due to superposition of diverging light waves reflected from the two sides of a plane parallel plate. A non-localized fringe pattern of high contrast was obtained as a function of incident angle when we used a coherent diverging beam. We found that the fringe density increased to a certain angle and then decreased thereafter, against the common belief that the fringe density increases monotonically with the angle of incidence. Because the fringe density is maximum at this angle and does not change rapidly in the vicinity, we could employ Fourier analysis to estimate interferometric parameters, such as thickness, refractive index, and wavelength, that determine the characteristic stationary fringe density.

Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.

Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping

This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85 % , when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions.

Modeling and Compensation of the Penetration Bias in InSAR DEMs of Ice Sheets at Different Frequencies

Interferometric synthetic aperture radar (InSAR) is able to provide important information for the characterization of the surface topography of glaciers and ice sheets. However, due to the inherent penetration of microwaves into dry snow, firn, and ice, InSAR elevation models are affected by a penetration bias. The fact that this bias depends on the snow and ice conditions as well as on the interferometric acquisition parameters complicates its assessment and makes it also relevant for measuring topographic changes. Recent studies indicated the potential for model-based compensation of this penetration bias. This article follows this approach and investigates the performance of two subsurface volume models for this task. Single-channel and polarimetric approaches are discussed for random and oriented volume scenarios. The model performance is assessed on two test sites in the percolation zone of the Greenland ice sheet using fully polarimetric airborne X-, C-, L-, and P-band InSAR data. The results indicate that simple models can partially compensate the penetration bias and provide more accurate topographic information than the interferometric phase center measurements alone.

Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier

ABSTRACTIn this paper, a decomposition scheme of the coherency matrix is presented to parse the information of polarimetric interferometric synthetic aperture radar (PolInSAR) images in detail. First, the decomposition method is improved by the polarimetric interferometric similarity parameter (PISP) to relief the overestimation occurred in the traditional four-component decomposition method. Second, after using the improved four-component decomposition results as the original inputs, the decomposition method is applied to retrieve scattering mechanisms or identify scatters, with the image separated into seven subsections. Finally, based on the modified decomposition results, the basic classification results are regarded as the feature training sets, and the Wishart classifier is then used as an optimized classification process. The applications of the decomposition and classification scheme are shown with typical representative L-band E-SAR images, which are used to show the robustness of the method, as well as with the first published airborne C-band PolInSAR data collected by the Institute of Electronics, Chinese Academy of Sciences, in November 2017. Experimental results demonstrate that the obtained decomposition and classification results are in good agreement with the actual physical scattering mechanisms.

A Hierarchical Extended Multiple-Component Scattering Decomposition of Polarimetric SAR Interferometry

In this letter, a hierarchical extended multicomponent decomposition method (ExMCSM) of polarimetric synthetic aperture radar interferometry (PolInSAR) is proposed. The target of this method is to overcome the overestimation of volume scattering (OVS) and to mitigate the mixed ambiguity of the scattering mechanism. In the proposed method, prior to the decomposition, orientation angle compensation (OAC), and helix angle compensation (HAC) are applied to the coherency matrix to reduce the complexity of the operation. The polarimetric interferometric similarity parameter (PISP) is used to refine the volume scattering models, and the optimal coherence magnitude (γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt3</sub> ) is used as a criterion for the adaptive selection of these volume scattering models. The efficiency of the proposed method is demonstrated using two different test sites. Experimental results demonstrate that the proposed method can effectively represent the scattering characteristics of the ambiguous regions.

RELATIONSHIP BETWEEN SAR/SENTINEL-1 POLARIMETRIC AND INTERFEROMETRIC DATA WITH BIOPHYSICAL PARAMETERS OF AGRICULTURAL CROPS

Abstract. The use of SAR (Synthetic Aperture Radar) data in agricultural applications has not yet been adequately explored, due to the complexity of the processing required, the lower diversity and availability of SAR data, and the difficulty in interpreting this data. The interactions between microwave energy and vegetation are influenced by factors related to plant structure, dielectric properties of the canopy, planting density and line orientation, and the angle of incidence and polarization of the wave. These differences between the recorded phases are used in data analysis techniques such as polarimetry and interferometry. Thus, the objective of this research is to analyse the relationships between the biophysical attributes of two agricultural crops (soybean and wheat) and the polarimetric and interferometric parameters of the SAR/Sentinel-1 data. The backscatter coefficients sigma and gamma in the VV polarization have inversely direct relation with the height of the crops, that is, as cultures grow, the interaction of energy in this polarization increases and the return signal decreases. For the cross polarization VH, the behaviour is the opposite, the larger the canopy height, the greater the interaction of vertical polarization and the greater the return on horizontal polarization. The interferometric coherence had small values, characterising a temporal decorrelation between the image pairs, due the canopy development. This preliminary study serves as a basis for future research with SAR/Sentinel-1 data focused on crops.

Weak value amplification for nonunitary evolution

We discuss interferometric parameter estimation of the amplitude, instead of the phase, via weak value amplification. The considered weak interaction introduces modulation on the amplitude of the wave function; therefore, the two-party state experiences a nonunitary evolution. With the same pre- and postselection states as those of original weak value amplification, a much larger anomalous amplification factor can be attained. The shift in the intensity profile at the dark port and the signal-to-noise ratio for the parameter of interest get further amplified by the weak value, compared to phase-based weak value amplification. Although the nonunitary evolution introduces loss, more information about the parameter of interest can be extracted. Thus the weak value amplification is extended to measurement of amplitude and nonunitary processes. We also discuss possible applications of this idea for precisely measuring tiny rotations or enhancing the image contrast of transparent objects.

Design of a 3D printed compact interferometric system and required phone application for small angular measurements.

A 3D printed smartphone based interferometric system is proposed, and its usability has been demonstrated by measuring small angular rotations. All necessary fringe processing and data analysis have been performed within the phone itself using custom designed application developed in an android platform. The main objective of the proposed work is to demonstrate the usability of modern smartphone and 3D printing technology for optical interferometric applications. The smartphone camera has been used to record the interference fringes which has been formed due to the change in the optical path difference (OPD) between light rays reflected from the top and bottom surface of a microscopic glass slide. The angular variation of the slide causes a detectable change in the OPD between the interfering beams which subsequently would cause a variation in the fringe pattern. By evaluating necessary interferometric parameters, small angular rotation can be computed within the smartphone application. With the designed smartphone based interferometric system, angular rotation as small as 0.02° can be measured accurately and reliably having a dynamic range of -3.68° to 3.68°. Due to the involvement of the smartphone as a platform for recording as well as onboard fringe processing, the designed interferometric system can be visualized as a truly field portable tool for different optical metrological applications.