Bistatic InSAR interferometry imaging and DSM generation for TH-2

TH-2 is a bistatic synthetic aperture radar (SAR) satellite system in formation flight. Compared with traditional InSAR systems, it can eliminate decoherent sources such as time and atmosphere, besides, it can generate highly coherent SAR image pairs. This paper firstly describe the extended chirp scaling (ECS) imaging algorithm based on the hyperbolic equivalent method, and also introduces pre-filtering to deal with problems such as reduced coherence and interference phase errors caused by mixed baselines. Secondly, it introduces the interference processing method and the technical process of DSM reconstruction in the bistatic mode. Finally, an interference imaging experiment is performed using the original echo data of a certain mountainous experimental area, and the 3D reconstruction experiment is performed by using the generated SAR image pair, which analyzes the coherence of the image, the phase unwrapping results and the DSM reconstruction results. The experimental results verify that the interference imaging algorithm in this paper has good focusing effect and phase preservation capacity. At the same time, the interferometry and 3D reconstruction capabilities of the data are verified as well

A Novel Channel Inconsistency Calibration Algorithm for Azimuth Multichannel SAR Based on Fourth-Order Cumulant

In a high-resolution and wide-swath synthetic aperture radar (SAR) platform, the along-track position error reduces the accuracy of the phase error estimation, which will lead to the failure of aliased signal reconstruction. However, classical subspace-based methods require at least one redundant subaperture to construct signal or noise subspace. To overcome this condition, a robust channel error estimation method is presented, which introduces the higher order cumulants to separate these two subspaces by increasing the spatial degree of freedom. First, the $M$ physically existing subapertures are expanded into $2M-1$ virtual channels to construct the noise subspace more accurately. Then, according to the expanded array configuration, the channel error model and the actual steering vector are modified. Finally, based on the orthogonality of signal and noise subspaces, two sets of constrained minimization formulations are constructed. Due to the coupling between these errors, the phase and along-track position errors can be obtained, respectively, by exploiting the idea of alternate iterations. Besides, compared with classical subspace-based methods, the proposed algorithm can avoid the subspace swap phenomenon under condition of low signal-to-noise ratios because the fourth-order cumulant can efficiently suppress additive Gaussian white noise. Finally, the well-focused SAR images, acquired by the four-channel airborne, GF3-01, and GF3-02 SAR systems, demonstrate the feasibility of the proposed error estimation method

Calibration of Airborne Interferometric SAR with Single Corner Reflector in Two Converse Flights

Quite a few corner reflectors are essential for interferometric SAR in high precision terrain mapping applications,which limits its application in surveying and mapping industry.In this paper,we present a calibration algorithm of airborne interferometric SAR using single corner reflector in two converse flights.Firstly,based on principle of SAR interferometry,a three-dimensional calibration model considering horizontal and elevation positioning is constructed.Then several characteristic parameters which affect 3D location are analyzed and reduced to three parameters successfully.Finally,we extracted a number of tie points from two groups of complex image pairs in two converse flights by SIFT algorithm.New calibration functions can be developed from the tie points,which helps reduce number of control points.Real data experiments results confirmed the validity and rationality of the proposed algorithm

An Epipolar HS-NCC Flow Algorithm for DSM Generation Using GaoFen-3 Stereo SAR Images

Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow method with normalized cross-correction constraint based on epipolar stereo images. First, the epipolar geometry is applied to resample the image to realize the coarse stereo matching. Subsequently, the EHNF method forms a global energy function to achieve fine stereo matching. The EHNF method constructs a local normalized cross-correlation constraint term to compensate for the grayscale invariance constraint, especially for the SAR stereo images. Additionally, two assessment methods are proposed to calculate the optimal cross-correlation parameter and smoothness parameter according to the refined matched point pairs. Two GaoFen-3 (GF-3) image pairs from ascending and descending orbits and the open Light Detection and Ranging (LiDAR) data are utilized to fully evaluate the proposed method. The results demonstrate that the EHNF algorithm improves the DSM elevation accuracy by 9.6% and 27.0% compared with the HS flow and multi-window ZNCC methods, respectively

Forest Height Estimation Based on Constrained Gaussian Vertical Backscatter Model Using Multi-Baseline P-Band Pol-InSAR Data

In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter profile (VBP) more accurately. However, the GVB model is highly complex, seriously reducing the inversion efficiency because of a number of variables. Given that concern, this paper proposes a constrained Gaussian Vertical Backscatter (CGVB) model to reduce the complexity of the GVB model by establishing a constraint relationship between forest height and the backscattering vertical fluctuation (BVF) of the GVB model. The CGVB model takes into account the influence of incidence angle on scattering mechanisms. The BVF of VBP described by the CGVB model is expressed with forest height and a polynomial function of incidence angle. In order to build the CGVB model, this paper proposes the supervised learning based on RANSAC (SLBR). The proposed SLBR method used forest height as a prior knowledge to determine the function of incidence angle in the CGVB model. In this process, the Random Sample Consensus (RANSAC) method is applied to perform function fitting. Before building the CGVB model, iterative weighted complex least squares (IWCLS) is employed to extract the required volume coherence. Based on the CGVB model, forest height estimation was obtained by nonlinear least squares optimization. E-SAR P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) data acquired during the BIOSAR 2008 campaign was used to test the performance of the proposed CGVB model. It can be observed that, compared with Random Volume over Ground (RVoG) model, the proposed CGVB model improves the estimation accuracy of the areas with incidence angle less than 0.8 rad and less than 0.6 rad by 28.57 % and 40.35 % , respectively

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively