Hannes Raggam

Forest Assessment Using High Resolution SAR Data in X-Band

Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.

Assessment of the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data

TerraSAR-X can acquire image data in various resolutions down to a range of about 1 m. Moreover, the sensor can operate at various imaging beams and thus acquire image data at different off-nadir viewing angles. These circumstances led to a stimulation of the traditional stereo-mapping approach, as TerraSAR-X image pairs became available in high resolution and in various geometric dispositions. With respect to 3-D surface mapping, TerraSAR-X stereo data processing, therefore, is a serious alternative to synthetic aperture radar interferometry, which can be addressed as the evolving mapping technique of the last decade. Within the TerraSAR-X science program of the German Aerospace Center (DLR), high-resolution multibeam data sets in Spotlight mode were acquired for several Austrian test sites. In general, three images were obtained from either ascending or descending orbits. In order to exploit the 3-D mapping accuracy of TerraSAR-X, stereo-radargrammetric mapping techniques were applied to the data sets, thereby utilizing stereo pairs as well as multi-image data sets in various dispositions. This paper focuses on one of the selected test sites and refers to the issues of 2-D and 3-D mapping-accuracy assessment as well as to surface model and vegetation-height-model generation. Validation of these products was widely restricted to visual analysis due to the lack of adequate high-quality reference products.

The Epipolarity Constraint in Stereo-Radargrammetric DEM Generation

For stereometric processing of optical image pairs, the concept of epipolar geometry is widely used. It helps to reduce the complexity of image matching, which can be seen to be the most crucial step within a workflow to generate digital elevation models. In this paper, it is shown that this concept is also applicable to the cocircular geometry of synthetic aperture radar (SAR) image pairs. First, it is proven that, for any feasible SAR acquisition, the deviation from true epipolar geometry is within subpixel range and therefore acceptably small. Based on this, we propose a method to create “epipolar” geometry for arbitrary stereo configurations of any SAR sensor through appropriate geometric image transformations. Consequently, the semiglobal matching (SGM) algorithm can be applied, which is restricted to epipolar geometry and is thus known to be highly efficient. This innovative approach, integrating both epipolar transformation and SGM, has been applied to a TerraSAR-X stereo data set. Its benefit has been demonstrated in a comparative assessment with respect to results, which have been previously achieved on the same test data using state-of-the-art stereometric methods.

Quality assessment of TerraSAR-X derived ground control points

The orbit accuracy of TerraSAR-X and TanDEM-X radar satellites is in the range of centimeters. This information is useful to carry out high precision surveying from space. The objective of this study is the quality assessment of ground control points (GCPs) retrieved from TerraSAR-X and TanDEM-X imagery. The focus is put on the optimized stereo constellation for the GCP retrieval procedure. GCPs are important inputs for precise orthorectification of other image sources. Depending on the input image parameters it is possible to retrieve GCPs with a 3D accuracy up to 1 m.

Using worldwide available TerraSAR-X data to calibrate the geo-location accuracy of optical sensors

A method to calibrate the geo-location accuracy of optical sensors is presented which is based on a novel multi-modal image matching strategy. This concept enables to transfer points from highly accurate TerraSAR-X imagery to optical images. These points are then used to register the images or to update the optical sensor models. The potential of the methodology is demonstrated on Spot 5, Ikonos and RapidEye images.

Monitoring mediterranean wildlands with very high spatial resolution satellite imagery for fire prevention and control

The feasibility to extract and visualise forest biometric information from Ikonos and QuickBird satellite imagery was demonstrated for a vegetated area in Portugal

Approaches to automate image geocoding and registration

Coregistration and geocoding are standard methods to generate registered stacks of multiple remote sensing image data. It is shown that pixel-based image matching is a useful tool to automate these procedures. Demonstration examples are provided for a multi-temporal set of ERS SAR images.

Atmospheric induced errors in interferometric DEM generation

Atmospheric error impacts on interferometric DEM generation were assessed. It was found that atmospheric effects are manifested in the InSAR data as deviations from the local topography of the terrain. Local height errors of 50 m corresponding to a phase shift of about 247/spl deg/ in an InSAR image pair with a baseline of 160 m was found, while the RMS error of the InSAR generated DEM was about 11 m. Error analysis indicated that atmospheric induced errors in InSAR DEMs are significantly reduced in larger baselines data.

DEM-based epipolar rectification for optimized radargrammetry

The quality of DEMs derived via radargrammetry mainly depends on the similarity of the SAR stereo images, taken under different look angles, in the image matching step. This work presents a novel pre-processing method that allows generating more similar epipolar images which are corrected by the underlying topography thus leading SAR specific distortion corrected stereo pairs. The evaluation w.r.t LiDAR data shows the increased quality of resulting DEMs.

Geometric performance of ENVISAT ASAR products

We describe validation measurements of the geometric accuracy of ASAR images, measured redundantly via independent methods. Our tests include image (IM), alternating polarization (AP), and wide swath (WS) mode acquisitions over a variety of test sites. ASARs slant range products (IMS/APS) require a slightly different validation methodology than ground range precision (IMP, APP) and medium resolution products (IMM, APM, WSM). A third approach is required for ellipsoid-geocoded products (IMG, APG). The most highly accurate validation is possible with single look complex (SLC) data (IMS and APS products), as all other product types lose resolution during multilooking. For a library of ground control points (GCPs) including map features such as bridges or road intersections, as well as (where available) transponders and corner reflectors, we use surveyed or map-measured position information (together with the delay value in the case of transponders) to solve the zero-Doppler iteration and predict the position of the GCP as an azimuth and slant range coordinate in the radar image. In the case of ground range products (e.g. IMP, APP, IMM, APM, WSM) the predicted slant range value is additionally transformed by a slant to ground range transformation tro determine the predicted image coordinate. The GCP feature is then either measured by inspection of a detected image, or localized automatically within the neighborhood of the prediction. GCPs are measured within the radar geometry image products, derivative geocoded products, and topographic maps, providing their measured map, radar geometry, and nominally geocoded GTC locations. Radar image locations are compared to map reference values and statistics of differences are tabulated. We compare the accuracies of the estimates achievable using transponders and map GCPs. Based on the suite of products (and accompanying orbit information) available to us, we establish a methodology for estimating a preliminary sampling window start time bias. The multiple validation and estimation techniques used ensure robust determination of ASAR geolocation accuracy.