Sahil Suri

Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas

The launch of high-resolution remote sensing satellites like TerraSAR-X, WorldView, and Ikonos has benefited the combined application of synthetic aperture radar (SAR) and optical imageries tremendously. Specifically, in case of natural calamities or disasters, decision makers can now easily use an old archived optical with a newly acquired (postdisaster) SAR image. Although the latest satellites provide the end user already georeferenced and orthorectified data products, still, registration differences exist between different data sets. These differences need to be taken care of through quick automated registration techniques before using the images in different applications. Specifically, mutual information (MI) has been utilized for the intricate SAR-optical registration problem. The computation of this metric involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g., SAR and optical). Satellites carrying high-resolution remote sensing sensors like TerraSAR-X and Ikonos generate enormous data volume along with fine Earth observation details that might lead to failure of MI to detect correct registration parameters. In this paper, a solely histogram-based method to achieve automatic registration within TerraSAR-X and Ikonos images acquired specifically over urban areas is analyzed. Taking future sensors into a perspective, techniques like compression and segmentation for handling the enormous data volume and incompatible radiometry generated due to different SAR-optical image acquisition characteristics have been rightfully analyzed. The findings indicate that the proposed method is successful in estimating large global shifts followed by a fine refinement of registration parameters for high-resolution images acquired over dense urban areas.

Applicability of the SIFT operator to geometric SAR image registration

The SIFT operators success for computer vision applications makes it an attractive alternative to the intricate feature based SAR image registration problem. The SIFT operator processing chain is capable of detecting and matching scale and affine invariant features. For SAR images, the operator is expected to detect stable features at lower scales where speckle influence diminishes. To adapt the operator performance to SAR images we analyse the impact of image filtering and of skipping features detected at the highest scales. We present our analysis based on multisensor, multitemporal and different viewpoint SAR images. The operator shows potential to become a robust alternative for point feature based registration of SAR images as subpixel registration consistency was achieved for most of the tested datasets. Our findings indicate that operator performance in terms of repeatability and matching capability is affected by an increase in acquisition differences within the imagery. We also show that the proposed adaptations result in a significant speed-up compared to the original SIFT operator.

Modifications in the SIFT operator for effective SAR image matching

With the increasing availability and rapidly improving the spatial resolution of synthetic aperture radar (SAR) images from the latest and future satellites like TerraSAR-X and TanDEM-X, their applicability in remote sensing applications is set to be paramount. Considering challenges in the field of point feature-based multisensor/multimodal SAR image matching/registration and advancements in the field of computer vision, we extend the applicability of the scale invariant feature transform (SIFT) operator for SAR images. In this article, we have analysed the feature detection, identification and matching steps of the original SIFT processing chain. We implement steps to counter the speckle influence, which deteriorates the SIFT operator performance for SAR images. In feature identification, we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFTs rotation invariance characteristic increases the potential number of matches when the multiple SAR images from different sensors have been acquired with the same geometrical acquisition parameters. In the feature matching stage, we propose to assist the standard SIFT matching scheme to utilise the SIFT operator capability for effective results in challenging SAR image matching scenarios. The results obtained for SAR images acquired by different sensors using different incidence angles and orbiting directions over both rural and semi urban land cover, highlight the SIFT operators capability for point feature matching in SAR imagery.

A new software/hardware architecture for real time image processing of wide area airborne camera images

This paper describes a new software/hardware architecture for processing wide area airborne camera images in real time. The images under consideration are acquired from the 3K-camera system developed at DLR (German Aerospace Center). It consists of three off-the-shelf cameras, each of it delivers 16 Mpixel three times a second. One camera is installed in nadir, whereas the other two cameras are looking in side direction. Main applications of our system are supposed to be automotive traffic monitoring, determining the workload of public road networks during mass events, or obtaining a survey of damages in disaster areas in real time. Altogether, this demands a fast image processing system on the aircraft, because the amount of original high resolution images can not be sent to ground by up-to-date transfer mode systems. The on-board image processing system is distributed over a local network. On each PC several modules are running concurrently. In order to synchronize several processes and to assure access to commonly used data, a new distributed middleware for real time image processing is introduced. Two sophisticated modules one for orthorectification of images and one for traffic monitoring are explained in more detail. The orthorectification and mosaicking is executed on the fast graphics processing unit on one PC, whereas the traffic monitoring module runs on another PC in the on-board network. The resulting image data and evaluated traffic parameters are sent to a ground station in near real time and are distributed to the involved users. Thus, with the here suggested software/hardware system it becomes possible to support rescue forces and security forces in disaster areas or during mass events in near real time.

Using geometric accuracy of TerraSAR-X data for improvement of direct sensor orientation and ortho-rectification of optical sattelite data

The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. It is due to the fact that it measures distances which are mainly dependent on the position of the satellite and the terrain height. If the used DEM is of high accuracy, the resulting geocoded data are very precise. This precision can be used to improve the exterior orientation and thereby the geometric accuracy of optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using mutual information and to estimate improvements for the attitude data through this information. By adjustment calculations falsely matched points can be eliminated and an optimal improvement can be found. The optical data are orthorectified using these improvements and the available DEM. The results are compared using conventional ground control information from GPS measurements.