Linear discontinuous ground deformation detection based on coherence analysis of pre and post event radar image pairs

The article presents information on how to use satellite interferometry to detect linear discontinuous ground deformation [LDGD] caused by underground mining. Assumptions were made based on the properties of the SAR signal correlation coefficient (coherence). Places of LDGD have been identified based on these assumptions. Changes taking place on the surface between two acquisitions lead to worse correlation between two radar images. This results in lower values of the SAR signal correlation coefficient in the coherence maps. Therefore, it was assumed that the formation of LDGD could reduce the coherence value compared to the previous state. The second assumption was an increase in the standard deviation of coherence, which is a classic measurement of variability. Therefore any changes in the surface should lead to increasing standard deviation of coherence compared to the previous state. Images from the Sentinel-1 satellite and provided by the ESA were used for analysis. The research is presented on the basis of two research areas located in the Upper Silesian Coal Basin in the south of Poland. The area in which LDGD could occur was limited to 6 % of the total area in case 1 and 36 % in case 2 by applying an appropriate methodology of satellite image coherence analysis. This paper is an introduction to the development of a method of detecting LDGDs caused by underground mining and to study these issues further.

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.