Multichannel interferometry synthetic aperture radar (InSAR) systems enable the reconstruction of terrain height profiles by integrating multiple interferograms obtained from multifrequency or multibaseline configurations. In this paper, we present a robust redundant remainder number system (RRNS) specifically designed for the high-precision determination of terrain height profiles. The proposed method consists of two main steps: First, erroneous remainders are clustered, and the common remainder is estimated optimally. Next, the integral portion is determined using the RRNS. Furthermore, we derive a robust estimation condition that generalizes existing results. To apply the proposed method to real data, we have extended the robust RRNS technique to accommodate practical data conditions. The main improvements are as follows: First, the phase unwrapping model for real numbers is adapted to the integral case by selecting suitable prime numbers. Second, the process of selecting the optimal reconstruction result is enhanced by incorporating redundancy congruence, which involves using two equations for each case. Simulation results demonstrate that the extended method outperforms both the least squares method and the minimum cost flow method.

Phase unwrapping (PU) is a critical step in interferometric synthetic aperture radar (InSAR) data processing. Among all PU methods, multibaseline PU (MBPU) methods is a state-of-the-art method, which can overcome the limitations of the phase continuity assumption in the traditional single-baseline PU methods. However, the MBPU methods still cannot effectively balance PU accuracy and efficiency when processing large-size interferograms. In particular, the current best MBPU methods, two-stage programming approach (TSPA), cannot work well when the baseline ratio is less than 2, limiting its application. To solve this problem, a WaveCluster-based robust and fast MBPU algorithm (WCRFPU) is proposed in this paper. First, appropriate initial grid and neighborhood parameters are selected according to the exclusive information of the InSAR data set to reduce the number of wrong clusters caused by dimension mismatch. Then the WaveCluster algorithm is used to cluster the intercept map with 3D clustering features, which can obtain more accurate clustering results and efficiently handle large-size interferograms. Subsequently, a cluster correction step is added to improve the PU accuracy further. Theoretical analysis and experimental results show that this method has more advantages than the existing MBPU methods in efficiency, accuracy, and adaptability to baseline ratios when processing large-size interferograms.

Multi-baseline (MB) Phase Unwrapping (PU) technology is an important step in Synthetic Aperture Radar Interferometry (InSAR). The related method of MBPU aims to improve the noise robustness of the Chinese Remainder Theorem (CRT). As one of the popular methods of MBPU, the existing MBPU method based on cluster analysis (CA) has some challenges that need to be improved in practical applications. For example, the clustering results are inaccurate, the time required to process large-scale interferograms is too long, and the noise clusters in the clustering results are not processed further. To address the above issues, this paper proposes a WaveCluster-based fast and large scale MBPU method (WCFLS). Firstly, the InSAR data set is preprocessed, and then the WaveCluster algorithm is used to process the data set. Finally, the noise clusters in the clustering results are firstly identified accurately, and then the cluster numbers are reassigned to the noise clusters. Theoretical analysis and experiments show that the proposed method has a better improvement in noise robustness and efficiency.

Although deep learning techniques have achieved noticeable success in aircraft detection, the scale heterogeneity, position difference, complex background interference, and speckle noise keep aircraft detection in large-scale synthetic aperture radar (SAR) images challenging. To solve these problems, we propose the geospatial transformer framework and implement it as a three-step target detection neural network, namely, the image decomposition, the multiscale geospatial contextual attention network (MGCAN), and result recomposition. First, the given large-scale SAR image is decomposed into slices via sliding windows according to the image characteristics of the aircraft. Second, slices are input into the MGCAN network for feature extraction, and the cluster distance nonmaximum suppression (CD-NMS) is utilized to determine the bounding boxes of aircraft. Finally, the detection results are produced via recomposition. Two innovative geospatial attention modules are proposed within MGCAN, namely, the efficient pyramid convolution attention fusion (EPCAF) module and the parallel residual spatial attention (PRSA) module, to extract multiscale features of the aircraft and suppress background noise. In the experiment, four large-scale SAR images with 1-m resolution from the Gaofen-3 system are tested, which are not included in the dataset. The results indicate that the detection performance of our geospatial transformer is better than Faster R-CNN, SSD, Efficientdet-D0, and YOLOV5s. The geospatial transformer integrates deep learning with SAR target characteristics to fully capture the multiscale contextual information and geospatial information of aircraft, effectively reduces complex background interference, and tackles the position difference of targets. It greatly improves the detection performance of aircraft and offers an effective approach to merge SAR domain knowledge with deep learning techniques.

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$L^{p}$-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.

Long-term monitoring of highways in soft soil areas, especially during the postconstruction period, is of great significance to ensure transportation safety and the quality of highway construction. Multitemporal interferometric synthetic aperture radar (MTInSAR) provides an effective tool for soft clay highway monitoring. However, most time-series models used in MTInSAR modeling are empirical mathematical functions, which ignores the physical properties of the observed objects and may limit the accuracy of the retrieved deformation and the understanding of the underground settlement dynamics. We propose a novel InSAR time-series deformation model (namely, NREM) with an emphasis on the rheological mechanisms for soft soil highways and environmental factors (temperature, humidity, and precipitation) to improve the accuracy of the traditional InSAR model and assist in the analyzing the rheological properties of soft soil. The NREM is constructed based on a combination of the seasonal model and the Burgers model introduced from the field of rheology. The primary parameters (i.e., viscosity and elastic modulus) are introduced in the NREM and estimated with the generation of time-series surface deformation. In the real data experiments, two highways are selected as the test areas. The results show that the standard deviations (STDs) of the high-pass deformation, which can reflect the modeling accuracy, derived by the NREM are lower than those of the three traditional models, yielding an improvement of 45% for the Lungui Highway (LH) and 50% for the G1508 Highway (GH). The root mean square errors (RMSEs) for deformation results derived from NREM are estimated to be ±5.1 mm compared with the leveling measurements, which outperforms the traditional models. The obtained rheological parameters can broaden the application of InSAR technology and provide a reference for highway engineering.

Phase unwrapping (PU) and phase filtering are the key procedures for the interferometric synthetic aperture radar (InSAR) technology. As one of the most popular multibaseline PU (MBPU) algorithms, the cluster-analysis (CA)-based MBPU algorithm still has some problems that need to be improved. To begin with, the cluster ambiguity vector is obtained by searching the nearest integer point to the cluster centerline with known slope and intercept in the search space. It will be time-consuming and inconvenient when the number of baselines or the search space is too large. In addition, they do not have the capacity of phase filtering. Moreover, they do not consider the impact of different baseline combinations on the performance of the CA-based MBPU algorithm. For these reasons, a novel CA-based MBPU and filtering (MBPUF) algorithm is proposed in this article. The main contributions of this article are that it gives the closed-form solving formulas of the cluster ambiguity vector to improve the efficiency of the CA-based MBPU algorithm, proposes a novel MB InSAR phase-filtering strategy that makes the CA-based MBPU algorithm capable of solving the phase-discontinuity problem and improving the height-reconstruction accuracy simultaneously, and utilizes the optimal baseline combination to improve the robustness of the CA-based MBPU algorithm. Theoretical analysis and experiments on both simulated and real MB InSAR data sets show the effectiveness and robustness of the proposed closed-form robust CA-based MBPUF algorithm.

Long-term surface deformation monitoring of highways is crucial to prevent potential hazards and ensure sustainable transportation system safety. DInSAR technique shows its great advantages for ground movements monitoring compared with traditional geodetic survey methods. However, the unavoidable influences of the temporal and spatial decorrelation have brought restrictions for traditional DInSAR on the application for ribbon infrastructures deformation monitoring. In addition, PS and SBAS techniques are not suitable for the area where adequate natural high coherent points cannot be detected. Due to this, we designed an integrated highway deformation monitoring algorithm based on CRInSAR technique in this paper, the processing flow including Corner Reflectors (CR) identification, CR baseline network establishment, phase unwrapping, and time series highway deformation estimation. Both the simulated and real data experiments are conducted to assess and validate the algorithm. In the scenario using simulated data, 10 different noise levels are added to test the performance under different circumstances. The RMSE of linear deformation velocities for 10 different noise levels are obtained and analyzed, to investigate how the accuracy varies with noise. In the real data experiment, part of a highway in Henan, China is chosen as the test area. Six PALSAR images acquired from 22 December 2008 to 09 February 2010 were collected and 12 CR points were installed along the highway. The ultimate time series deformation estimated show that all the CR points are stable. CR04 is undergoing the most serious subsidence, with the maximum magnitude of 13.71[Formula: see text]mm over 14 months. Field leveling measurements are used to assess the external deformation accuracy, the final RMSE is estimated to be [Formula: see text][Formula: see text]mm, which indicates good accordance with the result of leveling.

In this paper, a high precision algorithm to estimate initial phase offset fast is presented for airborne dual-antenna InSAR system. Firstly, the factors influencing the initial phase offset are analyzed, and then the influence of the initial phase offset to DEM is given. According to the analysis, the algorithm of real-time estimation of the initial phase offset with high precision is presented. In order to evaluate the performance of the algorithm, two sets of real airborne dual-antennal InSAR data from the Institute of Electronics, Chinese Academy of Sciences (IECAS) are processed. The results prove the algorithm can get superior precision initial phase offset in real-time, which is very useful for real-time InSAR system.

The gradually increase of highway ground deformation has caused more disasters, which indicates high potential of traffic danger. In this paper, an integrated highway ground deformation monitoring algorithm based on CRInSAR is designed and carried out. Both the simulated experiment and real data experiment have been designed and implemented in order to validate the algorithm proposed. Due to the flexibility and good backscatter characteristic of CR in SAR images, the method shows high precision in deformation detection on highway object. The result of the simulated experiment shows the ideal feasibility of the algorithm, while that of the real data experiment shows good performance in highway deformation monitoring. The method proposed can play significant importance in the prevention of traffic accident induced by the accumulated highway ground deformation.

Phase difference measurement of sinusoidal signals can be used for phase calibration in spaceborne/airborne single-pass InSAR system. However, there are little discussions about the selection of the sampling frequency on the undersampling condition and corresponding measuring method when the signal's frequency is too high. In order to solve the problem aforementioned, this paper proposes a modified method based on Coherent Accumulation and DFT. Firstly, the appropriate under-sampling frequency is chosen to sample the two sinusoidal signals with the same frequency. Then, the sampled signals are coherent accumulated with the period of the baseband signal. Thirdly, the accumulated sampled signals are used to calculate the initial phases of the two sinusoidal signals by using the Discrete Fourier Transformation. Lastly, the phase difference is gotten from the initial phases. Experiment results prove the effectiveness of the method.

Both in quality-guide phase unwrapping algorithms and weighted minimum-norm phase unwrapping algorithms, quality maps play a crucial role in obtaining the absolute phase from the wrapped ones. In this letter, a new technique for generating quality maps based on the gray level co-occurrence matrix (GLCM) is proposed. GLCM is a classical second-order statistics method for analyzing the texture features of images. Through exploring the second-order statistics of GLCM, much useful information in the image can be exploited. According to the characteristics of the interferogram, the second-order statistic of GLCM called “difference of entropy” is used to generate the quality maps. Besides, we modified the definition of “difference of entropy” to make the statistic more suitable for the problem. Finally, the new algorithm is compared with other conventional algorithms both in the simulated and real data experiments and the results show its better performance.

Interferometric synthetic aperture radar (InSAR) multichannel system, which produces more than one interferogram in a multifrequency or multibaseline configuration, allows us to reconstruct highly sloped and discontinuous terrain height profiles. In this letter, a novel method based on a closed-form robust Chinese remainder theorem (CRT) is presented to solve the height reconstruction problem. Proper reference remainder selection and remainder differential process, which do not exist in the traditional CRT, are adopted to improve the robustness of the height reconstruction. We also modify the method by exploiting statistical characteristics of interferometric phase and the combined information of InSAR interferograms. Moreover, a special weighted mean filtering method is adopted to get a better result. Experimental results prove the effectiveness of the method.

Interferometric synthetic aperture radar (InSAR) multichannel system, based on the collection of more interferograms acquired in a multifrequency or in a multibaseline configuration, allows us to reconstruct highly sloped and discontinuous terrain height profiles. In this paper, we present a new method to solve the digital elevation model (DEM) reconstruction problem using the closed-form robust Chinese remainder theorem (CRT). This method uses the remainder differential process, which does not exist in the traditional CRT, to improve the robustness of the DEM reconstruction. The simulation results demonstrate that the performance of the proposed method is better than statistical methods such as maximum likelihood (ML) estimation techniques, when the number of independent interferograms does not large enough.