A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Subsidence, especially in populated areas, is becoming a threat to human life and property. Monitoring and analyzing the effects of subsidence over large areas using in situ measurements is difficult and depends on the size of the subsidence area and its location. It is also time-consuming and costly. A far better solution that has been used in recent years is Differential Interferometry Synthetic Aperture Radar (DInSAR) monitoring. It allows the monitoring of land deformations in large areas with high accuracy and very good spatial and temporal resolution. However, the analysis of SAR images is time-consuming and involves an expert who can easily overlook certain details. Therefore, it is essential, especially in the case of early warning systems, to prepare tools capable of identifying and monitoring subsidence in interferograms. This article presents a study on automated detection and monitoring of subsidence troughs using deep-transfer learning. The area studied is the Upper Silesian Coal Basin (southern Poland). Marked by intensive coal mining, it is particularly prone to subsidence of various types. Additionally, the results of trough detection obtained with the use of convolutional neural networks were compared with the results obtained with the Hough transform and the circlet transform.

Section: Introductionmentioning

confidence: 99%

Monitoring Subsidence Area with the Use of Satellite Radar Images and Deep Transfer Learning

Franczyk

Bała

Dwornik

2022

“…Interferometric Synthetic Aperture Radar (InSAR) technology exploits differences in the phase of the waves returning to the satellite of at least two complex SAR images to generate, for instance, surface topography and deformation maps or Digital Elevation Models (DEMs) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Atmospheric artefacts are usually strongly correlated with the topography (elevation) and the proximity of the sea [11,21]. Over the past decades, numerous methods were investigated to identify and mitigate these artefacts e.g., [3,8,11,22,23].…”

Section: Introductionmentioning

confidence: 99%

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Chojka

Artiemjew

Rapiński

2020

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

“…The range phase-group delay difference method is based on the fact that the ionospheric phase and group delays are equal in magnitude, but opposite in sign [16,17]. The range split-spectrum technique uses the dispersive characteristic of the ionosphere and divides the range spectrum of the radar signal into two sub-bands to estimate the ionospheric phase component [18][19][20][21][22][23]. An 80 MHz bandwidth seems enough for the needed separation of the sub-bands [24].…”

Section: Introductionmentioning

confidence: 99%

Ionospheric Phase Compensation for InSAR Measurements Based on the Faraday Rotation Inversion Method

Wang

et al. 2020

The ionospheric error can significantly affect the synthetic aperture radar (SAR) signals, particularly in the case of L band and lower frequency SAR systems. The ionospheric distortions are mixed with terrain and ground deformation signals, lowering the precision of the interferometric measurements. Moreover, it is often difficult to detect the small-scale ionospheric structure due to its rapid changes and may have more influence on ionospheric phase compensation for InSAR measurements. In this paper, we present a Faraday rotation (FR) inversion method and corresponding procedure to compensate the ionospheric error for SAR interferograms and to detect the variations of small-scale ionospheric disturbances. This method retrieves the absolute total electron content (TEC) based on the FR estimation and corrects the ionospheric error for synthetic aperture radar interferometry (InSAR) measurements by transforming the differential TEC into the ionospheric phase. In two selected study cases, located in high latitude and equatorial regions where ionospheric disturbances occur frequently, we test the method using the Phased Array L-band Synthetic Aperture Radar (PALSAR) full-polarimetric SAR images. Our results show that the proposed procedure can effectively compensate the ionospheric phase. In order to validate the results, we present the results of ionospheric phase compensation based on the split-spectrum method as a comparison to the proposed method. To analyze the ability of our proposed method in detecting small-scale ionospheric disturbances, TEC derived from FR estimation are also compared with those derived from the global ionosphere maps (GIM). Our research provides a robust choice for the correction of ionospheric error in SAR interferograms. It also provides a powerful tool to measure small-scale ionospheric structure.