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Monitoring volcanic deformations allows us to track dynamic states of a volcano and to know where an eruptions could happen. Spaceborne Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) techniques created an opportunity to track volcanoes globally, even in inaccessible regions without ground measuring stations.This paper proposes a convolutional neural network (CNN) for detection of volcanic deformations in InSAR velocity maps. We had only a small amount of velocity maps over the region of central South American Andes, therefore the synthetic data are used to train the model from scratch. In the region of interest, the velocity maps contain the patterns of salt lakes and slope induced signal which confuse CNN models trained on synthetic data.In order to bridge the gap between the synthetic and real data, the hybrid synthetic-real data set is used for fine-tuning the model. The hybrid set consists of the real background signal data and synthetic volcanic data. Four fine-tuning sets which were created by different combinations of the original hybrid data, the filtered hybrid data, and simulated data have been used and compared with each other. Besides, we compared four fine-tuning approaches to determine where and how to fine-tune the model. Results show significant improvement in performance by majority of the approaches, and training the last or last two layers have given the best results. In addition, using the FT1 (containing only hybrid set), and FT4 (containing all sets) improved the area under the curve receiver operating characteristic (AUC ROC) from 55% to 86% and 88% respectively.

TecVolSA (Tectonics and Volcanoes in South America) is a project with a goal of developing intelligent Earth Observation (EO) data processing and exploitation for monitoring various geophysical processes in central south American Andes. Large amount of Sentinel-1 data over the period of about 5 years has been processed using mixed Permanent Scatterer and Distributed Scatterer (PS/DS) approaches. The received products are velocity maps with InSAR relative error in the order of 1 mm/yr on a large scale (>100km). The second milestone of the project was automatic extraction of information from the data. In this work, the focus is on detecting volcanic deformations. Since the real data prepared in such manner is limited, to train a deep learning model for detection of volcanic deformations, a synthetic training set is used. Models are trained from scratch and InceptionResNet v2 was selected for further experiments as it was found to give best performance among the tested models. The explainable AI (XAI) techniques were used to understand and analyze the confidence of the model and to understand how to improve it. The models trained on synthetic training set underperformed on real test set. Using GradCAM technique, it was identified that slope induced signal and salt lake deformations were mistakenly identified as volcanic deformations. These patterns are difficult to simulate and were not contained in synthetic training set. Bridging this distribution gap was performed using hybrid synthetic-real fine-tuning set, consisting of the real slope induced signal data and synthetic volcanic data. Additionally, false positive rate of the model is reduced using low-pass spatial filtering of the real test set, and finally by adjustments of the temporal baseline received from a sensitivity analysis. The model successfully detected all 10 deforming volcanoes in the region, ranging from 0.4 - 1.8 cm/yr in deformation.

Since its launch in 2007, TerraSAR-X has continuously provided spaceborne synthetic aperture radar (SAR) images of our planet with unprecedented spatial resolution, geodetic, and geometric accuracy. This has brought life to the once inscrutable SAR images, which deterred many researchers. Thanks to merits like higher spatial resolution and more precise orbit control, we are now able to indicate individual buildings, even individual floors, to pinpoint targets within centimeter accuracy. As a result, multi-baseline SAR interferometric (InSAR) techniques are flourishing, from point target-based algorithms, to coherent stacking techniques, to absolute positioning of the former techniques. This article reviews the recent advances of multi-baseline InSAR techniques using TerraSAR-X images. Particular focus was put on our own development of persistent scatterer interferometry, SAR tomography, robust estimation in distributed scatterer interferometry and absolute positioning using geodetic InSAR. Furthermore, by introducing the applications associated with these techniques, such as 3D reconstruction and deformation monitoring, this article is also intended to give guidance to wider audiences who would like to resort to SAR data and related techniques for their applications.

The Imaging Geodesy or Geodetic SAR technology exploits precise instrument metrology, wave propagation correction and dynamic Earth corrections following geodetic standards, e.g. solid Earth tides, to achieve an absolute pixel positioning accuracy comparable with GNSS. The achievable accuracy is in the low centimeter range, depending on the SAR system resolution, its metrology products such as orbit position and timing, on the SAR processor accuracy and on the accuracy of atmospheric information describing the neutral and dispersive path delay contributions. In the past the authors have demonstrated absolute positioning of corner reflectors with 5 cm accuracy in 3D space [2] and the positioning of natural points with a about 10 cm accuracy [3,5]. They have furthermore developed a pre-operational processor to add geodetic information to TerraSAR-X products [3]. An extension to other sensors is foreseen, especially Sentinel-1. Moreover, a large corner reflector (CR) array with more than 40 CRs distributed across some 150 by 150 kilometers has become available [4]. It is maintained by Geoscience Australia (GSA) and offers many possibilities in testing and verifying the said methods. Current work is focused on using alternative reflectors than conventional corner reflectors, exploiting the lower resolution TOPS mode of Sentinel-1 and to seek for more applications of this new geodetic method [5], [6]. The talk summarizes recent developments, i.e. - highly accurate TerraSAR-X geodetic measurements - experiences with new, higher resolution ECMWF data to model the neutral atmosphere - experiences with Sentinel-1 data on the team’s and the GSA corner reflector networks and - a study plan to extend the technology to Sentinel-1 data

Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute three-dimensional localization of natural Persistent Scatterer (PS)s which allows for Ground Control Point (GCP) generation using only SAR data. The prerequisite for the method to achieve high precision results is the correct detection of common scatterers in SAR images acquired from different viewing geometries. In this contribution, we describe three strategies for automatic detection of identical targets in SAR images of urban areas taken from different orbit tracks. Moreover, a complete work-flow for automatic generation of large number of GCPs using SAR data is presented and its applicability is shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the city of Oulu, Finland and a test site in Berlin, Germany.

The German TerraSAR-X and TanDEM-X satellites are characterized by unique features such as providing images with high spatial resolution and an unprecedented geometric accuracy. The latter has been significantly improved in the recent years by quantifying and removing the most prominent error sources which affect radar range and azimuth time measurements, a method called imaging geodesy [1]. Moreover, if corrected time observations of a specific target are available from SAR acquisitions with different viewing geometries, it has been demonstrated that the stereo SAR method is capable of delivering 3-D absolute coordinates of the target with accuracies in the decimeter to centimeter regime, depending on the target being a corner reflector or an opportunistic persistent scatterer (PS) [2]. As a first step towards the inclusion of such accurately localized point targets into phase-based stacking methods, in [3] the concepts of imaging geodesy and stereo SAR were used to transform the relative estimates of SAR tomography (TomoSAR) into absolute 3-D point clouds by absolutely localizing the reference point. The improvement in the localization accuracy of the resulting point cloud has encouraged us to continue expanding the mentioned framework by automatic detection and absolute localization of useful PS candidates which are visible from SAR images acquired from different viewing geometries, either same-heading or cross-heading orbit tracks. This will generate multiple Ground Control Points (GCPs) which can be used as a reference network in multi-pass InSAR techniques for reliable estimation and removal of atmospheric phase screen and for support in phase-unwrapping. The availability of such points can also be relevant for non-InSAR applications such as detection of large magnitude motions which are invisible from InSAR time-series approaches or as tie points for improving the registration of remotely sensed optical images. This contribution is dedicated to introducing an automated processing chain for generating GCPs from SAR data. The procedures start with the identification of high quality PS candidates, in some strategies with the aid of external data, to precisely extract PS timings in a stack of non-corregistered SAR images. The subsequent steps are the correction of PS timings and the absolute localization with the Stereo SAR method. It will also be demonstrated how these GCPs can be used to improve the geocoding accuracy of InSAR, in particular PSI, point clouds leading to providing absolute positions. Finally, preliminary results based on TerraSAR-X high resolution spotlight images over the city of Oulu, Finland is reported.

Very high resolution SAR images in dense urban area are not trivial to interpret due to the inevitable layover caused by the side-looking imaging geometry. With the growing attention on very high resolution SAR data, the fusion of optical and SAR images in dense urban area has become an emerging and timely topic, because the complementary of these two data types can lead us to unprecedented insights and findings, such as the unique scattering mechanisms of different urban infrastructures. Lying at the basis of such fusion topic is the challenging task of the co-registration of SAR and optical images. Such two images are acquired with intrinsically different imaging geometries, and thus are nearly impossible to be co-registered without a precise 3-D model of the imaged scene. Only until recently, the “SARptical” system proposed a promising solution to tackle this challenging task. SARptical can trace individual SAR scatterers in corresponding high resolution optical images where we can analyze the geometry, material, and other properties of the imaged object. Vice versa, the similar study can also be done in the SAR image coordinate. This paper demonstrate the capability of the SARptical system, and its potential in various different applications and research directions.

In this paper, we propose a framework referred to as “geodetic TomoSAR“ that fuses the SAR image geodesy and TomoSAR approaches to obtain absolute 3D positions of a large amount of natural scatterers. The methodology is applied on four Very High Resolution (VHR) TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since the TomoSAR estimates are referred to the identical reference point whose absolute 3D positions are retrieved by means of Stereo-SAR, the point clouds from ascending and descending orbits are automatically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3D TomoSAR point clouds are compared to a DSM obtained by airborne LiDAR.

Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image. Based on the radar viewing geometry, we propose that this problem can be formulated as a bounding box regression problem and therefore allows for integrating height data from multiple data sources in generating ground truth on a larger scale. We introduce building footprints from geographic information system (GIS) data as complementary information and propose a bounding box regression network that exploits the location relationship between a building's footprint and its bounding box, allowing for fast computation. This is important for large-scale applications. The method is validated on four urban data sets using TerraSAR-X images in both high-resolution spotlight and stripmap modes. Experimental results show that the proposed network can reduce the computation cost significantly while keeping the height accuracy of individual buildings compared to a Faster R-CNN based method. Moreover, we investigate the impact of inaccurate GIS data on our proposed network, and this study shows that the bounding box regression network is robust against positioning errors in GIS data. The proposed method has great potential to be applied to regional or even global scales.