Airbus Defence and Space Gmbh

Eads Deutschland Gmbh

Christoph Stahl

Winfried Lohmiller

Thomas Fechner

Oliver Rockinger

Martin Hanel

Benjamin Benninghofen

Holger Leuck

Tamer Koban

Heiko Seidel

A method for effecting the airborne determination of geographic coordinates of corresponding pixels from digital synthetic aperture radar images, where the SAR images are available in the form of slant range images and the recording position of the respective SAR image is known. The coordinates of the corresponding pixels in the SAR images and the corresponding range gates are used in each case to determine the distance between a corresponding resolution cell on the ground and the respective recording position of the respective SAR image. The determined distances and associated recording positions of the SAR images are used to determine the geographic coordinates of the corresponding pixels in the SAR images by employing a WGS84 ellipsoid.

Method for determining the geographic coordinates of pixels in SAR images

An automatic landing system contains a control device for providing positional data for controlling an aircraft, a first position or range measuring device for detecting first positional data of the aircraft, a second position or range measuring device for detecting second positional data of the aircraft, and a sensor device for detecting sensor data from which a direction in which a landmark is located and/or a distance of the landmark to the aircraft can be determined. The control device may be configured to generate, based on the first positional data, a first hypothesis for the direction and distance of the landmark and, based on the second positional data, a second hypothesis for the direction and distance of the landmark. Moreover, the control device may be configure to confirm or discard the first hypothesis and the second hypothesis, respectively, using the sensor data detected by the sensor device.

Redundant determination of positional data for an automatic landing system

Techniques for the detection of moving objects in a video image sequence are provided. Distinctive image variation points in a difference image are determined. An image variation threshold value is established and image variation points are determined as those points in the difference image in which the absolute image brightness value of which exceeds the image variation threshold value. The quality of the image variation points are determined based on at least one predetermined quality criterion. If the quality criterion is met, the determined image variation points determined are determined as the distinctive image variation points determined. Otherwise, the establishment of an image variation threshold value and determination of the quality of image variation points is repeated with a changed image variation threshold value.

Method and device for the detection of moving objects in a video image sequence

Image data of optically acquired input images () are processed for emphasizing at least two object classes. Each pixel is subjected to a rough classification () based on first criteria that determine whether or not a pixel is relevant for an object recognition. A reduced image () is formed from the relevant pixels and irrelevant pixels are omitted. The reduced image () is filtered () for forming at least two correlated filtered images () based on second criteria. Classified images (A,A,A) are formed from the filtered images by classifiers that work in accordance with predetermined rules. Weighting factors are allocated to each object class. The classified images are merged in accordance with an algorithm to make a combined global evaluation for each object class. The global evaluation decides, based on the merged images (A,B,C), for each pixel whether the respective pixel belongs to an object class and if so to which object class.

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Karlskron, Germany

Christoph Stahl