Change detection using very high resolution SAR images is an important source of information for reconnaissance applications. Modern SAR sensors are capable of acquiring many images in short periods of time, which creates the need for a reliable automatic change detection method. In this paper, we will describe a new automatic change detection approach that combines very high resolution SAR images with prior knowledge about the imaged scene. In this case, the prior knowledge about the scene will come from vector maps, which can be obtained from a Geographic Information System (GIS). These vector maps will allow us to determine which regions are of interest for the change detection, and what kind of changes/objects can be expected there. The algorithm described in this paper will be applied to a time series of high resolution TerraSAR-X images of a port with military shipyards, and used to automatically detect ship activity and extract information about the detected ships. In this case, the vector maps were obtained from a Geographic Information System (GIS) containing map data from OpenStreetMap
Circular synthetic aperture radar (CSAR) can provide a full aspect coverage on interesting scenes in one run. Over the city of Karlsruhe a Ka-band dataset was generated in CSAR mode. The data was focused using subapertures in a step of 1.5°, each SAR image representing the scene from a slightly different aspect. The potential of non-coherent fusion of full aspect coverage to reveal small targets was demonstrated. By a manual selection of the viewing angle, parking cars next to high buildings could be revealed and a full view on selected targets with reduced shadow and overlay effects was shown. We studied the effect of varying aspects on the focused image pixels and developed a first metric to automatically select the best viewing angle to a local scene. Areas containing ground information like grass or asphalt and which are not hidden between high objects could be identified and used to deliver a good aspect view on neighboring areas which suffer from shadowing effects.
Positioning a patient accurately in treatment devices is crucial for radiological treatment, especially if accuracy vantages
of particle beam treatment are exploited. To avoid sub-millimeter misalignments, X-ray images acquired from within the
device are compared to a CT to compute respective alignment corrections. Unfortunately, deviations of the underlying
geometry model for the imaging system degrade the achievable accuracy. We propose an automatic calibration routine,
which bases on the geometry of a phantom and its automatic detection in digital radiographs acquired for various
geometric device settings during the calibration. The results from the registration of the phantom's X-ray projections and
its known geometry are used to update the model of the respective beamlines, which is used to compute the patient
alignment correction. The geometric calibration of a beamline takes all nine relevant degrees of freedom into account,
including detector translations in three directions, detector tilt by three axes and three possible translations for the X-ray
tube. Introducing a stochastic model for the calibration we are able to predict the patient alignment deviations resulting
from inaccuracies inherent to the phantom design and the calibration. Comparisons of the alignment results for a
treatment device without calibrated imaging systems and a calibrated device show that an accurate calibration can
enhance alignment accuracy.
To align patients in radiation devices in six degrees of freedom (DoF), image-guided approaches perform the task of correction computation for the patient position. Digital radiography (DR) images are compared to projections of a CT series to estimate misalignments. A problem is that digital reconstructed radiographs (DRRs) have to be created from the CT to be registered with the DRs. Depending on the X-ray tube energy, detector sensitivity and body part involved, DRRs and DRs may look very different and often cannot be registered. We present a method that reconstructs multi-spectral DRRs for different X-ray settings, which can be registered to real X-ray images. As short rendering times are crucial, multiple spectra of a DRR are generated in one ray-tracing process. We register our multi-spectral DRR with the DR and add a further DoF to find a best match not only for the translations and in-plane rotation, but also the best fitting spectral planes. The results are used to identify patient misalignments and show that higher reliability can be achieved compared to conventional approaches. Misalignments can be identified even if ineligible X-ray settings have been used. As our approach allows application of lower X-ray energies for DR creation, an additional benefit is the reduction of the delivered dose.
Current pulsed laser radar systems for ranging purposes are based on time-of-flight techniques. Nowadays first pulse as well as last pulse exploitation is used for different application, e.g. urban planning, forestry surveying. Besides this technique of time measurement the complete signal form over the time might be of interest, because it includes the backscattering characteristic of the illuminated field. This characteristic can be used for estimating the aspect angle of a plane with special surface property or estimating the surface property of a plane with a special aspect angle. In this paper a monostatic bi-directional experimental system with a fast digitizing receiver is described. The spatio-temporal beam propagation, the spatial reflectance of the surface, and receiver properties are modeled. A time dependent description of the received signal power is derived and our special surface property is considered. The spatial distribution of the used laser beam was measured and displayed by the beam profile. For a plane surface under various aspect angles the transversal distributions of the beam were simulated and measured. For these angles the corresponding temporal beam distributions were measured and compared with their pulse widths. The pulse spread is used to estimate the aspect angle of the illuminated object. The statistics for different angles was calculated. Different approaches which detect a characteristic time value were compared and evaluated. The consideration of the signal form allows a more precise determination of the time-of-flight. A 3-d visualization of equi-irradiance surfaces allows to access the spatio-temporal shape of the pulses.
A syntax oriented method for a map-aided analysis of structures in aerial images is proposed. At first the map must be analyzed in order to obtain a suitable representation of its knowledge content. A special kind of graph, a so-called image-description graph is the result of this map analysis. The knowledge of the map represented on different description levels is used to control the image analysis process. Based on this knowledge, expectations for attribute values of image objects are defined. Generated objects are assessed relative to the expectations of the map and the object model. A set-oriented selection method is applied to deduce the processing priority using these two assessments. Expected objects are preferably processed for building up more complex objects. Thus the map-aided analysis can be used to reduce processing time for the verification.
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