Antje Thiele

Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

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.

Building Detection From One Orthophoto and High-Resolution InSAR Data Using Conditional Random Fields

Todays airborne SAR sensors provide geometric resolution in the order well below half a meter. Many features of urban objects become visible in such data. However, layover and occlusion issues inevitably arise in urban areas complicating automated object detection. In order to support interpretation, SAR data may be analyzed using complementary information from maps or optical imagery. In this paper, an approach for building detection in urban areas based on object features extracted from high-resolution interferometric SAR (InSAR) data and one orthophoto is presented. Features describing local evidence as well as context information are used. Buildings are detected by classification of those feature vectors within a Conditional Random Field (CRF) framework. Although as graphical model similar to Markov Random Fields (MRF), CRFs have the advantage of incorporating global context information, of relaxing the conditional independence assumption between features, and of a more general integration of observations. We show that, first, CRFs perform well in comparison to Maximum Likelihood classifiers and MRFs. Second, the combined use of optical and InSAR features may improve detection results.

Feature Extraction and Visualization of Bridges Over Water From High-Resolution InSAR Data and One Orthophoto

Modern airborne SAR sensor systems provide geometric resolution in the order well below half a meter. By SAR interferometry from pairs of such images, DEM of the same grid size can be obtained. In data of this kind, many features of urban objects become visible, which were beyond the scope of radar remote sensing only a few years ago. However, because of the side-looking SAR sensor principle, layover and occlusion issues inevitably arise in undulated terrain or urban areas. Therefore, SAR data are difficult to interpret even for senior human interpreters. Furthermore, the quality of the InSAR DEM may vary significantly depending on the local topography. In order to support interpretation, SAR data are often analyzed using additional complementary information provided by maps or other remote sensing imagery. In this paper, object feature extraction and visualization from high-resolution InSAR data and one orthophoto is discussed for the example of a scene containing several bridges over water. Bridges are key elements of man-made infrastructure. Monitoring of these important connecting parts of the traffic network is vital for applications such as disaster management or in the context of political crisis, for instance, to evacuate inhabitants and to deliver goods and equipment. Aims of the approach are to derive key features of the bridges geometry from the complementary data sources, to determine the water level, smooth the noisy InSAR DEM data, especially at water surfaces, and, finally, to generate an improved 3-D visualization of the scene by overlapping the optical image on the InSAR DEM.

Building Reconstruction from Multi-aspect InSAR Data

Modern space borne SAR sensors like TerraSAR-X and Cosmo-SkyMed provide geometric ground resolution of one meter. Airborne sensors (PAMIR [Brenner and Ender 2006], SETHI [Dreuillet et al. 2008]) achieve even higher resolution. In data of such kind, man-made structures in urban areas become visible in detail independently from daylight or cloud coverage. Typical objects of interest for both civil and military applications are buildings, bridges, and roads. However, phenomena due to the side-looking scene illumination of the SAR sensor complicate interpretability (Schreier 1993). Layover, foreshortening, shadowing, total reflection, and multi-bounce scattering of the RADAR signal hamper manual and automatic analysis especially in dense urban areas with high buildings. Such drawbacks may partly be overcome using additional information from, for example topographic maps, optical imagery (see corresponding chapter in this book), or SAR acquisitions from multiple aspects.

Analysis of Gable-Roofed Building Signature in Multiaspect InSAR Data

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.

Building extraction in urban scenes from high-resolution InSAR data and optical imagery

Modern space borne SAR sensors provide geometric resolution of one meter, airborne systems even higher. In data of this kind many features of urban objects become visible, which were beyond the scope of radar remote sensing only a few years ago. However, layover and occlusion issues inevitably arise in undulated terrain and urban areas because of the side-looking SAR sensor principle. In order to support interpretation, SAR data are often analyzed using additional complementary information provided by maps or other remote sensing imagery. The focus of this paper is on building extraction in urban scenes by means of combined InSAR data and optical aerial imagery.

Feature extraction of gable-roofed buildings from multi-aspect high-resolution InSAR data

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.

Modeling and analyzing InSAR phase profiles at building locations

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.

Parameter Determination by RapidEye and TerraSAR-X Data: A Step Toward a Remote Sensing Based Inventory, Monitoring and Fast Reaction System on Forest Enterprise Level

State forest administrations in Central Europe have to adapt to future climatic and socioeconomic conditions. This results in new demands for up-to-date and precise forest information—especially with regard to the increase of forest damages by natural hazards. Remote Sensing techniques are appropriated for delivering information in support of such tasks. We present details of a research project that focuses on the demonstration of the potential of satellite data for forest management planning and disaster management. Integrated in the over-all concept of a decision support system (DSS) for the forest–wood chain (Entscheidungs-Unterstutzungs-System Forst-Holz, EUS-FH), the frame conditions for a ‘Remote Sensing based Inventory and Monitoring System’ for the forest-wood chain are developed. Particular focus is on investigations towards synergistic and complementary use of the two German satellite systems RapidEye and Terra SAR-X. The comparison is done on base of the accuracy of parameter derivation with each of the systems. The results deliver a couple of arguments for combined multispectral and SAR data use for monitoring and fast response situations in case of sudden calamities. But it reveals as well that the references against the results should be compared and, at the end, which represents the data layers to be updated, do not always fit from both, the semantic meaning e.g., the definition of ‘forest’ to cartographic differences, and the representation of object categories. Harmonisation of definitions and categories to be mapped is needed.

Automated detection of storm damage in forest areas by analyzing TerraSAR-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.