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
Specific imaging effects that are caused mainly by the range measurement principle of a radar device, its much lower frequency range as compared to the optical spectrum, the slanted imaging geometry and certainly the limited spatial resolution complicates the interpretation of radar signatures decisively. Especially the coherent image formation which causes unwanted speckle noise aggravates the problem of visually recognizing target objects. Fully automatic approaches with acceptable false alarm rates are therefore an even harder challenge.<p> </p>At the Microwaves and Radar Institute of the German Aerospace Center (DLR) the development of methods to implement a robust overall processing workflow for automatic target recognition (ATR) out of high resolution synthetic aperture radar (SAR) image data is under progress. The heart of the general approach is to use time series exploitation for the former detection step and simulation-based signature matching for the subsequent recognition. This paper will show the overall ATR chain as a proof of concept for the special case of airplane recognition on image data from the space borne SAR sensor TerraSAR-X.
Freight transportation service by truck is an extremely growing market all over the world. Consequently, optimization of truck’s capacity utilization by in-situ estimation of load distribution with a fast and stand-off monitoring sensor is useful. MWs or MMWs used in radars and radiometers can penetrate thin dielectric walls like synthetic truck canvas. Such systems can deliver also valuable information for security applications, e.g. about illegal transportation attempts. This paper describes the application of DLR’s experimental MW radar and MMW radiometers used for estimation of truck load under controlled driving conditions of a test truck. Experimental imaging results of both systems are presented.
In general, interpretation of signatures from synthetic aperture radar (SAR) data is a challenging task even for the expert image analyst. For the most part, this is caused by radar specific imaging effects, e.g. layover, multi-path propagation or speckle noise. Specifically for the application in maritime security, ship signatures exhibit additional defocusing effects due to the ship’s movement even when they are anchored. Focusing on object recognition, the detection of target signatures can be done with a pretty good chance of success, but the identification is often impossible. To assist image analysts in their recognition tasks, a SAR simulation tool has been developed recently. It is very simple to operate, by simulating available 3D model data of ships and test the resulting simulated signatures with their real counterpart from SAR images. This is a very robust way to identify larger vessels out of current one meter resolution space borne SAR data. Nevertheless, for smaller vessels this can be still very challenging, because the resolution is too coarse. Recently, TerraSAR-X initiated a new staring spotlight imaging mode that enhances cross-range resolution significantly and therefore also improves the chance for the identification of smaller vessels. This paper demonstrates the capabilities of the developed simulation tool in assisted target recognition specifically on ship signatures. The improvement of recognition performance will be studied by comparing results for TerraSAR-X sliding spotlight mode and staring spotlight mode data.
By means of data from highly resolved tower-turntable ISAR measurements this paper gives an overview of our work on
an ATR process from the raw data acquisition to the final ATR performance evaluation. Main objectives are the radar
imaging, the ATR robustness against small changes in the articulation of targets (e.g. military vehicles) and changes in
the incidence angle. The recognition process is based on a template matching method. The two-dimensional templates
are generated by extracting the most robust scatterers from the RCS image.
Imagery data acquired by recently launched space borne SAR systems demonstrate a very good spatial resolution (e.g.
one meter with TerraSAR-X). The designs of such complex systems make it compulsory to do SAR end-to-end
simulations to optimize image quality (e.g. spatial and radiometric resolution, ambiguity suppression, dynamic range,
etc.). The most complex, critical and challenging modules have to be designed for the generation of SAR raw data and
SAR image generation, because the limits of computability and memory requirements are reached very quickly.
Moreover, the analysis of SAR images is a demanding task, because of their sensor specific effects. Therefore, a
simulation tool is under development to analyze realistic target features and make the scattering processes transparent to
With the method presented in this paper, SAR images of complex scattering bodies can be generated in a very efficient
way. This is done by directly localizing scattering centers and identifying their persistency along the synthetic aperture.
Thus the usual raw data generation and processing steps are dropped. The resulting images show a very good similarity
to reality, because scattering centers due to multipath propagation effects are also handled. Furthermore this toolkit
makes it possible to visualize the scattering centers and their evolution, by mapping them on the 3D structure of the
scattering body. This results in transparency of the whole scattering process, which greatly improves the understanding
of the image effects. The paper presents this new approach for the application of inverse SAR (ISAR) and first