The sewer/water leakage has become a worldwide concern. Conventional field measurement method is cost-consuming and time-inefficient, and deep learning (DL)-based method is not readily to be interpreted. Under this condition, we propose an interpretable principal component analysis (PCA) and support vector machine (SVM)-based leak detection algorithm (PSLDA) for identifying the sewer/water leakage. The PSLDA takes synthetic aperture radar (SAR)-derived moisture content and SAR interferometry (InSAR) closure phase as inputs, conducts feature extraction and dimensionality reduction with PCA, and implements binary classification with SVM, finally identifying leaks or non-leaks. The main advantage of PSLDA lies in that it respectively adopts the mathematically-derived PCA and SVM instead of the convolutional and pooling layer and the loss-function layer in the DL network, ensuring the validity and interpretability simultaneously. With 1222 in-situ leak points and 1206 non-leak points in Beijing and Tianjin of China, the PSLDA is trained. The well-trained PSLDA is subsequently applied to detect leak/non-leaks in real-world scenarios, and achieves a leak-detection accuracy of 91.86% in the fifth ring road of Beijing, a non-leak-detection accuracy of 88.10% in the Huizhou City of Guangdong Province. The proposed PSLDA demonstrates to be efficacious and credible, potentially offering explicit guidance for pipeline maintenance.

The differential interferometric synthetic aperture radar (DInSAR) technique can estimate the Earth’s surface displacement theoretically at centimeter- or millimeter-level accuracy, where the two-dimensional phase unwrapping (PU) is ineluctable, so that improper PU algorithm would deteriorate the accuracy. Conventional single-baseline (SB) PU-based DInSAR cannot accurately monitoring high-gradient deformation due to the limitation of the phase continuity assumption. The conventional multibaseline (MB) PU-based DInSAR, including multi-frequency-based two-pass DInSAR and two-stage programming approach (TSPA)-based three-pass DInSAR, is capable of delineating high-gradient deformation but imposes stringent requirements on InSAR data. Under this condition, we present a multi-temporal baseline PU approach (MTBA) for two-pass DInSAR from single radar system such that even high-gradient deformation can be delineated at centimeter- or millimeter-level accuracy. The MTBA demonstrates to be valid through a simulation experiment and three strike-slip earthquake events. Furthermore, the MTBA expands the perpendicular baseline-based MB PU for topography mapping into temporal baseline-based MB PU domain for deformation monitoring, potentially enriching the MB PU theory and extending the application of DInSAR technique.

This paper aims to address two primary issues using the multi-temporal (MT) spaceborne SAR observations and InSAR technique to monitor slope stability in rugged terrains. One is how to maximize the observation and application effectiveness based on the geometric distortion in an area of interest (AOI) for determining appropriate ascending- and descending-orbit SAR observations. The other is how to optimize the selection of ground control points (GCPs) to correct orbital phase errors and reference in deriving valid ground surface displacements in the SBAS InSAR analysis. Four critical criteria were proposed to identify valid GCPs efficiently and effectively. The 2017 Xinmo landslide event, China, was showcased. The tear-away zone was 100% and ~50% distorted when imaged by ascending- and descending-orbit Sentinel-1A/B SAR sensors. The descending SAR datasets were preferred. Then, we identified four spatially dispersed GCP clusters with the selection criteria using the SBAS InSAR technique and 45 descending-orbit MT SAR datasets. One GCP per cluster was chosen. With the four GCPs and SBAS analysis, we successfully detected the stable status in the landslide-free area and the pre-landslide acceleration movement within the tear-away zone. Valid InSAR-derived results have been obtained. In turn, the GCP selection criteria have been validated, and the two issues resolved.

Interferometric phase linking has become a critical step in distributed scatterer (DS) interferometry, yet its performance is significantly affected by inaccuracies in the coherence matrix and its inverse, such as the precision matrix. Recent studies have attempted to address this issue using regularization techniques, modifying diagonal elements or shrinking the coherence matrix towards the identity matrix. However, these methods do not sufficiently resolve the problem. In this article, we propose a novel regularized coherence matrix estimation approach to enhance interferometric phase linking. Specifically, we introduce a coherence matrix estimator based on maximum likelihood estimation, constrained by the multivariate totally positive of order 2 ($ \rm{MTP}_{2}$), also called total positivity. A coordinate-descent algorithm is then applied to efficiently estimate both the coherence and precision matrices. Simulation experiments validate the effectiveness of the proposed approach, particularly in precision matrix estimation. Furthermore, real data experiments conducted at Chengdu Tianfu International Airport and Chongqing Jiangbei International Airport demonstrate that our approach outperforms three state-of-the-art coherence matrix regularization techniques.

Accuracy assessment of interferometric synthetic aperture radar (InSAR) products without external reference data (ERD) has been a long-standing challenge because traditional phase unwrapping (PU) is an ill-posed problem. The limitations in accuracy assessment make it impossible to verify the precision of the PU results under actual observation conditions, so that to be unknowable the reliability of InSAR products in practical applications. However, multibaseline (MB) PU is well-posed, so its accuracy can be evaluated in a statistical sense without using ERD, i.e., even if there is no in situ data, the accuracy of the products generated by MB InSAR can still be assessed theoretically. In this paper, through obtaining the closed form optimality condition of the Chinese Remainder Theorem (CRT) optimization model, the new independent quantitative index for MB PU accuracy evaluation was mathematically established. Interestingly, we found that i) the optimality condition is a sufficient and necessary condition for the accuracy assessment of the MB PU; ii) it is affected by the normal baseline lengths of the MB InSAR system and the interferogram noise intensity. To practically apply this mathematical condition, a deep convolutional neural network (DCNN) was developed for refining MB InSAR product accuracy. The validity and effectiveness of the proposed approach have been systematically verified using simulated and acquired interferometric datasets.

The interferometric synthetic aperture radar (InSAR) is able to reconstruct the Earth's surface topography with a meter-level accuracy when two-dimensional phase unwrapping (PU) is properly implemented. The two-stage programming approach (TSPA) can convert the ill-posed PU problem into a well-posed problem by integrating perpendicular baseline diversity in multiple ( $\ge 2$ ) interferograms, and is currently among the most commonly used multibaseline (MB) PU algorithms. Nevertheless, TSPA still faces two challenges in real-world applications: i) TSPA cannot ensure exceptional performance for any complex topographic scenarios, and ii) The PU error of short-baseline interferometric pair tends to propagate into the PU solution of long-baseline interferometric pair, degrading height accuracy. To overcome these issues, a refined TSPA (R-TSPA) MB PU algorithm is proposed in this paper. R-TSPA contains two phase unwrapping procedures under the framework of TSPA, where procedure 1 unwraps the flattened interferograms with TSPA, and procedure 2 re-estimates and re-unwraps the erroneous ambiguity number gradients with TSPA. It is demonstrated that R-TSPA outperforms the conventional single-baseline (SB) PU algorithms and TSPA with actual InSAR datasets in western Sichuan Province and Tibet Autonomous Region of China, revealing its potentials in accurately mapping topography and broadening application scopes of InSAR.