FUSION AND 3D VISUALIZATION OF HIGH-RESOLUTION INSAR DATA AND OPTICAL IMAGERY

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, a fusion approach of one high-resolution InSAR data set and one aerial image is presented. The fusion with optical imagery is particularly important because such data is available in large quantities. Even in the case of developing countries, image data can usually be provided. Algorithms already demonstrated in [1] are further developed and new results are shown.
Our semi-automatic fusion approach is feature based and consists of several components. Both, radiometric and geometric aspects are treated comprehensively in order to achieve highly accurate fusion results. Aims of the approach are to smooth the noisy InSAR DEM data, to derive key features of the buildings and bridge’s geometry from the complementary data sources, and finally to generate an improved 3D visualization of the scene by data fusion. In order to reduce noise, the input intensity images of the InSAR set as well as the optical image are preprocessed. After this smoothing operation, features are extracted. Different feature types may be thought of, such as regions, points and lines. In urban areas lines appear in high quantities due to man-made structures like buildings and bridges. Therefore, line features are extracted in both the SAR data and the optical image. For the optical image, the well known Canny-Operator is used whereas the asymmetric fusion of lines algorithm proposed in [2] is applied to the SAR data. In the next step, distance maps are calculated from the line feature images with the Danielsson algorithm [3]. So far, the optical and the InSAR imagery have been treated separately. They have been preprocessed, lines have been extracted and distance images have been computed. All such steps can be considered as preparations for the registration of the images following up. In order to register both optical and InSAR data, a registration framework implemented in the open source software library OTB (ORFEO Toolbox) [4] is used. As input to the registration framework, a master and a slave image have to be defined. In this case, the distance map derived from the feature image of the InSAR intensity image is considered the slave image. The distance map of the optical feature image is the master image, respectively. The slave image is then registered onto the master image by means of a similarity measure [5]. Such similarity measure evaluates to which extent we can find corresponding information in both the optical and the SAR imagery. Since we have reduced the information contained within the images to distance maps of line features, corresponding lines are considered corresponding information. A geometrical transformation is applied to the slave image in order to register it onto the master image. Rigid and non-rigid transformations may be thought of in order to model geometrical differences between optical and SAR data. However, previous research showed that rigid transformations are not capable of registering high resolution SAR imagery onto on optical image with high accuracy [6]. Due to the different sensor geometries, local residuals stay in the images and hence have to be dealt with. Therefore, a non-rigid transformation is chosen for the mapping of the SAR imagery onto the optical image.
After a fusion of the optical image and the InSAR data has been achieved with sufficient accuracy, the InSAR DEM can be smoothed with information provided by the optical image. In particular, noisy water surfaces in the InSAR DEM are smoothed using complementary information provided from the optical image. Additionally, a classification of the merged imagery can take place. This classification profits from complementary information available through the joint use of optical and SAR imagery. Heights provided by the InSAR DEM facilitate both the classification of the optical and the SAR imagery. Features extracted in the optical image can also enhance the InSAR DEM. Finally, preliminary results of our approach are demonstrated using a scene containing several bridges. 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, e.g. to evacuate inhabitants and to deliver goods and equipment.

[1] Soergel, U.; Thiele, A.; Cadario, E.; Thoennessen, U., 2007. Fusion of High-Resolution InSAR Data and optical Imagery in Scenes with Bridges over water for 3D Visualization and Interpretation. Proceedings of Urban Remote Sensing Joint Event 2007, URBAN, 6 p.
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[3] Danielsson, P.E., 1980. Euclidean Distance Mapping. Computer Graphics and Image Processing, vol. 14, pp. 227 - 248
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[5] Inglada, J.; Giros, A., 2004. On the possibility of automatic multisensor image registration. IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2104 - 2120
[6] Wegner, J., 2007. Automatic Fusion of SAR and Optical Imagery. Diploma Thesis accomplished at the Institute of Photogrammetry and GeoInformation, Leibniz University of Hannover, p. 79