As is well known, both conventional differential synthetic aperture radar interferometry (D-InSAR) and multi-temporal synthetic aperture radar interferometry (MT-InSAR) have a common limitation that they only can measure the deformation component along the radar line of sight (LOS) direction. However, in the majority of disaster investigations, there is more interest in the vertical deformation component than that of the horizontal direction, for example, in measuring ground subsidence in urban areas, or ground subsidence due to underground mining. To estimate the vertical deformation component accurately, it is in theory necessary to create at least three independent equations, and solve the vertical, the North–South, and the East–West deformation components by exploiting at least three-track InSAR LOS measurements or combining at least two-track InSAR LOS measurements with azimuth measurements. However, these methods are greatly limited and sometimes not even practical because there is typically little chance of obtaining a three-track SAR data set covering the same area in the same time span, and the accuracy of the azimuth measurements is far lower than that of InSAR LOS measurements. In this article, we found that it is possible to solve for the vertical deformation component from two-track InSAR LOS measurements in some circumstances. Then, an approach for accurately retrieving the vertical deformation component from two-track InSAR LOS measurements is proposed, and the analytical expression is presented. The approach is illustrated through an investigation of the ground subsidence in an area of Beijing, China. Unlike previous methods, this approach can accurately retrieve the vertical deformation component from two-track InSAR LOS measurements, and provide more reliable results for improving the interpretation of ground subsidence phenomena.

Permafrost areas pose a threat to the safe operation of linear projects such as the Qinghai–Tibet railway due to the repeated alternating effects of frost heaving and thawing settlement of frozen soil in permafrost area. Time series InSAR technology can effectively obtain ground deformation information with an accuracy of up to millimeters. Therefore, it is of great significance to use time series InSAR technology to monitor the deformation of the permafrost section of the Qinghai–Tibet railway. This study uses multi-time InSAR (MT-InSAR) technology to monitor the deformation of the whole section of the Qinghai–Tibet railway, detect the uneven settlement of the railway roadbed in space, and detect the seasonal changes in the roadbed in the time domain. At the same time, the local deformation sections over the years are compared and discussed. The time series deformation monitoring results of the permafrost section Sentinel-1 data in 2020 show that the length of the railway roadbed from Tanggula station to Za’gya Zangbo station (TZ) section is approximately 620 m, the deformation of the east and west sides is uneven, and the average annual deformation difference is 60.68 mm/a. The impact of frozen soil in WangKun station to Budongquan station (WB) section on railway roadbed shows the distribution characteristics of high in the middle and low at both ends, and the maximum annual average settlement can reach −158.46 mm/a. This study shows that the deformation of permafrost varies with different ground layers. The impact of human activities on frozen soil deformation is less than that of topography and hydrothermal conditions. At the same time, the study determined that compared with other sections, the roadbed deformation of TZ and WB sections is more obvious.

Nowadays, real-time monitoring of highway operation by unmanned aerial vehicle (UAV) technology is one of the research frontiers for urban remote sensing. In general, the existing stitching algorithms can meet the basic requirements in terms of accuracy, but their splicing speed cannot meet the real-time stitching requirements of UAV. The cause is that the time consumption sharply increases when stitching plenty of UAV images—this is the bottleneck problem. Herein, we proposed a novel splicing method based on the Superpoint network and a self-designed algorithm of matrix iteration. In this method, we take advantage of an advanced deep learning algorithm—Superpoint to efficiently extract image feature points for calculating the geometric transformation matrix, and make the Superpoint model more suitable for highway. More importantly, for the purpose of further improving the stitching speed and realizing real-time stitching for a large number of UAV images, we specially designed an algorithm of matrix iteration to accurately represent the image transformation relationships, i.e., a matrix is iterated through each adjacent transformation matrix relationship. It is the first time that an algorithm of transformation matrix iteration has been designed to address the bottleneck problem in stitching plenty of UAV images. As a result, the experiments indicate that the proposed method has remarkably enhanced the stitching speed and accuracy for plenty of UAV images. Notably, even in the condition of no air triangulation parameters, it can realize real-time stitching.

The interferometric coherence degree is a very important indicator of the quality of interferometric phase values in interferogram; while the existence of residues is a stubborn problem for phase unwrapping. Both of them are the vital factors influencing the performance of phase unwrapping algorithms in interferometric synthetic aperture radar (SAR) processing. In general, residues tend to be located in the regions with low coherence degree, which is just a qualitative description for the relationship between interferometric coherence degree and probability of residue occurrence. In order to further reveal the quantitative relationship between them, we actually used the residue density in the interferogram as an approximate substitute for the probability of residue occurrence, and then calculated the Pearson correlation coefficients between the ensemble coherence average and the residue density in the corresponding interferogram with several kinds of data from different SAR sensors, including ERS1/2, JERS-1, and Envisat-1 ASAR. The calculation results show a very strong inverse correlation between them, the Pearson correlation coefficients range from -0.702 to -0.963. In other words, the interferometric coherence degree not only can indicate the quality of interferometric phase, but also to a great extent reflect or even predict the density of residues in the interferogram using the linear regression analysis.

As is well known, perpendicular baselines coming close to the critical baseline will most likely give poor results or even not give any results in conventional SAR interferometry. In the present paper, we put forward a new concept named ‘maximal effective baseline’ for conventional SAR interferometry, which is intrinsically different from the concept of ‘critical baseline’. It is defined as ‘an upper limit of perpendicular baseline for specific SAR system, and it is impossible to obtain necessary coherence to implement conventional SAR interferometry as long as the perpendicular baseline of SAR image pair is beyond this upper limit’. Then, the theoretical approach to determine the maximal effective baseline for conventional SAR interferometry is presented, and the concrete values of maximal effective baselines for ERS1/2 and JERS are yielded by analysing various decorrelation sources. Further more, some InSAR experiments have been made to confirm the results. The ‘maximal effective baseline’ for conventional SAR interferometry is of practical significance in improving the working efficiency of conventional SAR interferometry.