Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.

The Sentinel-1 provides an unprecedented opportunity for InSAR research and applications, especially in the field of fast and accurate damage assessment, thanks to its extra wide swath, short revisit interval and free policy. Challenges also exist in Sentinel-1 TOPSAR interferometric processing, for example, the coregistration of TOPSAR images requires an accuracy of 0.001 pixels to reduce the phase jumps at the burst overlap region to 3°. To obtain the accuracy of 0.001 pixels for the coregistration of a stack of multi-temporal TOPSAR images, joint estimation method and network-based method were proposed and implemented statically. However, when new images are added, the existing methods cannot coregister them kinematically. In order to resolve this issue, we firstly give a brief review for the existing static methods, including the single master-, temporally transferred- and network-based methods, for coregistering multi-temporal TOPSAR images. Then we propose a kinematic coregistration method to coregister newly added TOPSAR images by introducing the Sequential Weighted Least Square Adjustment. Experimental results demonstrate that the proposed method can achieve an accuracy of 0.001 pixels for kinematic coregistrations of multi-temporal TOPSAR images. Compared with the static network-based coregistration method, the proposed method is superior in terms of both coregistration accuracy and computational efficiency. It will contribute a great deal to the globally acquired big SAR data (e.g., Sentinel-1 TOPSAR) and their near real-time processing.

Interferometric baseline estimation is a key procedure of interferometric synthetic aperture radar (SAR) data processing. The error of the interferometric baseline affects not only the removal of the flat-earth phase, but also the transformation coefficient between the topographic phase and elevation, which will affect the topographic phase removal for differential interferometric SAR (D-InSAR) and the accuracy of the final generated digital elevation model (DEM) product for interferometric synthetic aperture (InSAR). To obtain a highly accurate interferometric baseline, this paper firstly investigates the geometry of InSAR imaging and establishes a rigorous relationship between the interferometric baseline and the flat-earth phase. Then, a baseline refinement method without a ground control point (GCP) is proposed, where a relevant theoretical model and resolving method are developed. Synthetic and real SAR datasets are used in the experiments, and a comparison with the conventional least-square (LS) baseline refinement method is made. The results demonstrate that the proposed method exhibits an obvious improvement over the conventional LS method, with percentages of up to 51.5% in the cross-track direction. Therefore, the proposed method is effective and advantageous.

Determining the geographic location and spatial distribution of underground goaf is of great significance for the prevention of mining subsidence hazards and the detection of illegal mining. However, traditional goaf detection techniques mainly focus on geophysical methods that are labor intensive, have low efficiency, and are expensive. Due to the large range and off-site monitoring capability of interferometric synthetic aperture radar (InSAR) techniques, research on goaf location detection based on InSAR measurements has been increasing. This paper proposes a new method for locating underground goaf based on cross-iteration and InSAR measurements. Firstly, the functional relationship between the geometric parameters of the goaf and the line of sight (LOS) deformation retrieved by InSAR techniques is constructed. Then, the three initial model parameters of the probability integration method (PIM) are determined by mining geological conditions. Finally, the cross-iteration method is used to determine the parameters to characterize the spatial location of underground goaf. The experimental results show that the average relative errors of the simulated experiment and the real experiment are 1.5% and 5.1%, respectively, and the inverted goaf parameters are in good agreement with the real values. Moreover, the proposed method only requires the main lithology of the overlying rock in the goaf and does not depend on the accuracy of PIM model parameters. Therefore, this method has engineering application value for the detection of goaf lacking actual measurement data or that caused by illegal mining.

Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, using the strategy of leveling network adjustment, this paper proposes an improved PU method based on hierarchical networking and constrained adjustment. This method not only limits the phase error transfer of low-quality points, but also takes the PU results of high-quality points as control points and uses the network adjustment method with constraints to unwrap low-quality points, which effectively inhibits the influence of noise and improves the accuracy of unwrapping. Regardless of the unwrapping method used for high-quality points, the unwrapping accuracy of low-quality points can always be improved. Compared with other traditional two-dimensional phase unwrapping workflows, this method can more accurately recover the phase of low-coherence regions only through the interferogram. A simulation experiment showed that the local noise of the interferogram was effectively inhibited, and the PU accuracy of the low-quality regions was improved by 16–46% compared with different traditional methods. For a real-data experiment of mining area with low coherence, the PU result of our proposed method had fewer residues and lower phase standard deviation than traditional methods, further indicating the practicability and robustness of the proposed method. The work in this paper has considerable practical significance for recovering the decoherence phase with serious local noise such as mining centers and groundwater subsidence centers.

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas.