Monitoring the surface deformation of infrastructure elements such as road networks, bridges, dams, or levees is important since atypical behavior often indicates issues that arise. Advanced Differential Synthetic Aperture Radar Interferometry techniques, such as Persistent Scatterer Interferometry, are well-equipped for this task. However, the observed deformation rates for infrastructure elements can be relatively fast in the context of a Persistent Scatterer Interferometry processing. Thus, phase unwrapping may be difficult. In this paper, a concept to integrate a priori knowledge generated from freely available advanced Differential Synthetic Aperture Radar Interferometry datasets provided by the European Ground Motion Service into Persistent Scatterer Interferometry processing using the post-processing tool PSDefoPAT is introduced to ease phase unwrapping.

Monitoring the deformation of bridges with advanced Differential Synthetic Aperture Radar Interferometry techniques can provide relevant information concerning their untypical behavior. The availability of Synthetic Aperture Radar images increased immensely with the launch of the Sentinel-1 satellites. This led to the development of the European Ground Motion Service, which directly provides maps of the mean deformation velocity and the corresponding displacement time series of most European countries. In this paper, we demonstrate the applicability of these datasets to observe and study the deformation of bridges in an alpine environment.

The simulation of Synthetic Aperture Radar (SAR) images plays an important role for many applications such as Automatic Target Recognition, damage detection or for the in-detail analysis of signatures found in real SAR images. The need for creating synthetic SAR images has increased with the advent of Convolutional Neural Networks (CNNs), since, in contrast to the Electro-Optical domain, only a few very specialized data sets of SAR images exist that could be used for training CNNs. Today, several academic and commercial SAR simulators exist. For all of these, the foundation for the simulation is a 3D model of the scene or object to be simulated. Such 3D models exist in large quantities and can be obtained for virtually any object imaginable. However, these models were not specifically designed for SAR simulation and thus the question arises whether they are suitable for the task. In this paper, several aspects of 3D models are highlighted that should be considered when assessing them for SAR simulation.

Machine learning methods have proven to be a powerful tool for the classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. Since well-established pixel- or image-based classifiers expect real-valued input data, the information content of complex-valued PolSAR data needs to be represented by real-valued descriptors. This paper proposes a method to enable the generation of suitable descriptors without the explicit extraction of hand-crafted and preselected polarimetric features. For this purpose, the neighbor graph based dimension reduction method Uniform Manifold Approximation and Projection (UMAP) is applied to translate pixel similarity measured by the revised Wishart distance to real-valued 3-dimensional feature descriptors. Land cover classification results performed on a real-world dataset, show that the resulting 3-dimensional feature descriptor which allows a fast and memory efficient training, provides a similar level of information compared to an exhaustive feature set composed of 30 polarimetric features.

Embankment dams are often used to supply their surrounding region with freshwater, hydro-energy or manage floods and are thus of great importance for the region. However, should the dam body burst, it would be an economic loss for the re-gion and threaten the environment, infrastructure, and human life. Therefore, monitoring embankment dams is crucial, es-pecially during the first filling. It is the time period in which the structure experiences the load of the impounded water for the first time. The technique Persistent Scatterer Interferom-etry is well-positioned for this task. Persistent Scatterer Inter-ferometry is a remote sensing technique and thus facilitates infrastructure monitoring in hardly accessible areas. In addition, the technique provides two-dimensional information on the surface deformation instead of sporadic pointwise infor-mation. One end product is a map of the mean velocity of the surface deformation of the observation target in line of sight of the sensor for a specific observation period. In this paper, we present the challenges of creating a surface deformation map for the Parapeiros-Peiros dam. The dam body is excepted to experience a significant amount of defor-mation within a small area during its first filling, which makes identifying persistent scatterers challenging. Hence, we also discuss different approaches described in the literature to select persistent scatterers and their suitability for this specific application.

As an important source of food and protective habitat for other species living in the eulitoral, mussel beds have an important impact on the coastal ecosystem. To realize a frequent monitoring, we proposes the use of airborne polarimetric Synthetic Aperture Radar (SAR) data to meet the challenge of limited time windows for data acquisition. For distinguishing mussel beds from surrounding tidal flats, the suitability of six different polarimetric features, based on the Freeman Durden decomposition, the Kennaugh element framework and target depolarization effects, are analyzed. Based on the most promising features, pixel-based classification is performed using an encoder-decoder Convolutional Neural Network (CNN). The resulting regions identified as mussel covered are compared to verified reference data, generated three and five years before. Detected changes are additionally compared by visual alignment with current optical aerial imagery. The study demonstrates the potential of airborne polarimetric SAR data for the generation of up-to-date maps of mussel beds.

The detection of changes based on remote sensing data is a highly frequented field of research with several of important applications. In [1], a concept for change analysis in urban areas was proposed. The main part of this concept is the categorization of detected changes. Aiming on the changes itself, it can be distinguished between objects, which are static at the time of image acquisition and objects, which are in motion. In SAR image analysis, the detection of moving objects is commonly known as Ground Moving Target Indication (GMTI). For two-channel SAR systems like TerraSAR-X (TSX), a traditional method is Along-Track Interferometry (ATI). In our method [1], changes caused by static objects have been investigated using repeat-pass SAR imagery with a time gap of at least 11 days. This dataset excludes the application of ATI. Furthermore, ATI is only capable of detecting moving objects with motion in across-track direction [5]. In this paper, a GIS based detection scheme is proposed, which considers objects moving in along-track as well as in across-track direction at the time of image acquisition.

The advantages of SAR sensors for remote sensing applications have been proven many times already. Especially due to the high resolutions that the new generation of SAR sensors (e.g. TerraSAR-X, COSMO-Skymed) can achieve, those are becoming very interesting for the analysis of urban areas. Up to now, mainly interferometric data are used for retrieving the 3D information of building, due to their precise phase information. However, such methodology suffers from the relatively long time span that is required to obtain the data (e.g. TerraSAR-X repeat-pass: 11 days). SAR radargrammetry, on the contrary, has the advantage that the required acquisitions are obtained in shorter time spans, what can be helpful in cases where rapid response is on demand. By matching corresponding points of a stereo image pair, surface height can be retrieved. Our approach propose to determine 3D building parameters, especially height information, relying on the radargrammetric analysis of building layover areas. In this paper, we newly combined a hierarchical matching approach with a matching criterion based on the coefficient of variation. Our method shows improvement if compared to previous works.

The distribution of coherent targets takes a key role in the assessment of the performance and the evaluation of the processing results of multi-temporal Synthetic Aperture Radar interferometry (InSAR) approaches. In previous studies, the evaluation of the spatial distribution of coherent targets was largely based on rather simple parameters such as the spatial point density. While these parameters provide information on the total number of coherent points in an area, they are often insufficient to fully describe their spatial distribution. Hence, with this paper, new descriptors are introduced that better characterize the spatial distribution of coherent targets in an interferogram and serves as a better indicator of InSAR performance. A quantitative study is established both via simulated and real SAR data and the performance of the new parameters are discussed to demonstrate the capability of the developed parameters in describing the spatial distribution of coherent targets.

The dominant error source for short wavelength spaceborne radar signals is due to water vapor present in the neutral atmosphere (neutrosphere). This distortion signal is characterized by high variations in time and space, and can be exploited as a valuable source for quantifying the water vapor content of the Earth's atmosphere. Available water vapor measurements provided by Envisat Medium Resolution Imaging Spectrometer (MERIS) and simulations from numerical weather prediction models are still limited in observing rapid fluctuations of water vapor. Therefore, we are investigating Interferometric Synthetic Aperture Radar (InSAR) for water vapor mapping. In this paper, water vapor maps derived from Persistent Scatterer InSAR (PSI), MERIS, and the Weather Research and Forecasting (WRF) model are presented with comparative analyses.

The high-resolution space borne sensor TerraSAR-X shows good ability for extracting height information by use of repeat-pass interferometry. Indeed, the interferometric phase is still noisy due to temporal decorrelation. The new TanDEM-X mission offers for the first time the possibility of performing high resolution single-pass space borne interferometry, providing good coherence, and thus allowing a better mapping of the 3D shape of objects. These data are still affected by noise though and a filtering is prerequisite in order to enhance object recognition. In previous work, we presented several filtering methods in order to reduce the interferometric noise at building location. We particularly showed the benefits of introducing GIS information such as building footprints into the filtering. In this paper, we present a new filtering method, consisting of combining GIS-supported and area filters for better consideration of large building shape. Results of the GIS supported filter and of the new combined filter are presented on TanDEM-X data.

Fast mapping of storm-damaged forest areas is in great demand. In general, airborne platforms are called into action to get a quick impression and to record high-resolution data. However, such storm events come often along with bad weather conditions that limit acquisition of optical data as well as flying by airplane. In this case, the new generation of high-resolution spaceborne SAR sensors (e.g., TerraSAR-X) can be used to acquire rapidly image data. The new generation of high-resolution spaceborne sensors increases the expectation of more promising results. In this paper, we focus first on the border line extraction of forest areas to enable a fast estimation of wind-thrown areas, whereby the pre-event forest border is derived from multi-spectral data. Second, clean-up operations are monitored in the affected forest area by applying a change detection operator.

Active remote sensing techniques, like SAR tomography and full-waveform LIDAR, are able to capture the 3D reflectance function at or inside objects. They are therefore of special interest for analyzing forest environments. Research goals are the derivation and characterization of the different physical measurement aspects of data taken over forested areas, as well as establishing mutual relations in such a way that LIDAR data can be used to calibrate and correct 3D density data of SAR tomography. The paper outlines the phenomenology of forested areas in SAR tomograms and full-waveform LIDAR data and sketches a simple mathematical methodology for linking SAR and LIDAR reflection density profiles.

Today space-borne high resolution SAR sensors (e.g., TerraSAR-X, TanDEM-X, SAR-Lupe or Cosmo-SkyMed) provide SAR images up to spatial resolutions of 1-3m and even better in spotlight modes. Hence, one major issue of these missions is the development of methods to automatically derive detailed cartographic information from their data. Especially, the analysis of rural and urban areas is on demand in case of disasters (e.g., earthquakes), where active remote sensing systems are highly attractive. Here, an important issue is the development of automatic methods for damage assessment or change detection, in general. For that it is advisable to combine existing GIS data with current SAR data. In this paper an approach for 3D building reconstruction is presented, based on information fusion by utilizing GIS and InSAR data. Thereby, the GIS data are providing the 2D building footprints and the acquired InSAR data the height information. An InSAR simulation step and the subsequent assessment between the real and simulated InSAR phase data enables the extraction of the current building shape.

The spatial resolution of state-of-the-art synthetic aperture radar sensors enables the structure analysis of urban areas. The appearance of buildings in magnitude images in settlements is dominated by the effects of the inherent oblique scene illumination. In urban residential districts, salient pairs of parallel lines of bright magnitude are often caused by direct reflection and double-bounce signal at gable-roofed buildings. In this letter, the magnitude and interferometric phase signature of gable-roofed buildings are discussed to extract reliable building features for reconstruction. The analysis contains signature changes by varying illumination and building geometry. The presented approach is aiming at the reconstruction of gable-roofed buildings by a knowledge-based analysis considering the discussed effects. The reconstruction results are assessed by using a high-resolution LIDAR surface model as ground truth.

The main advantages of SAR are the capability of imaging large areas in short time and delivering data at any day time and under nearly all weather conditions. This is especially important for disaster management and continuous long term monitoring applications. Key elements of man-made infrastructure are bridges. Especially for bridges over water, the SAR specific side looking imaging geometry can lead to special characteristics in the image. In this paper, the possibilities of extracting bridge features like width and height from SAR data, especially for bridges over water, are discussed. The feature extraction is based on the segmentation of parallel lines in an image. An approach is presented to exploit this feature extraction for change detection. The investigations are supported by SAR simulations, and real airborne and spaceborne data are presented.

The achievable spatial resolution of state-of-the-art synthetic aperture radar (SAR) sensors enables the analysis of urban areas. The appearance of buildings in magnitude images is governed by effects of the inherent oblique scene illumination, such as layover, radar shadow and salient lines of bright scattering caused by direct reflection or multipath signal propagation. For example, in urban residential districts often salient pairs of parallel lines of bright magnitude are observed at locations of gable-roofed buildings. The first line (closer to sensor) is due to direct reflection of planar roof parts orientated toward the sensor. The second line can be related to signal caused by a dihedral corner reflector between ground and building wall. In this paper an approach is presented aiming at reconstruction of gable-roofed buildings by knowledge based analysis considering the mentioned SAR-specific effects. First, line and edge primitives are segmented and grouped to parallel line pair objects. Then for each of these objects geometrical and radiometrical features are extracted in the InSAR images. Based on the interferometric elevation data in the adjacent area of the primitives the projection from slant range into ground range geometry is done. After geocoding, building hypotheses are built from the fused set of extracted primitives from both aspect directions. The estimation of the building height is carried out by two complementing methods: One is based on the extracted geometric parameters and the other on interferometric height data. The reconstruction results are quantitatively assessed by using a high resolution LIDAR surface model as ground truth data.

The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. Even in the case of InSAR, for building recognition usually the analysis is triggered mainly from features detected in the magnitude images. However, considering InSAR data significant phase profiles in range direction at building locations are observable. In this paper a simple model for these characteristic profiles in layover areas is proposed. The model takes into account that in layover areas a mixture of several signal sources contribute to the interferometric phase of a range cell. At building locations a combination of contributions from ground, wall, and roof is observed. The resulting phase profiles are characterized by sensor and illumination parameters as well as object parameters. In the first part of this paper, simulated phase images at building locations based on a given surface profile in range direction are presented. In the second part, real InSAR data sets of the airborne sensors AeS-1 (Intermap) and AER-II (FGAN-FHR) with a slant range resolution of 38 cm respectively 94 cm are used to calculate interferometric phase truth data which are then compared with the simulation results.

The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.