Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage
The project DEKO (Detection of artificial objects in sea areas) is integrated in the German DeMarine-Security project and focuses on the detection and classification of ships and offshore artificial objects relying on TerraSAR-X as well as on RapidEye multispectral optical images. The objectives are 1/ the development of reliable detection algorithms and 2/ the definition of effective, customized service concepts. In addition to an earlier publication, we describe in the following paper some selected results of our work. The algorithms for TerraSAR-X have been extended to a processing chain including all needed steps for ship detection and ship signature analysis, with an emphasis on object segmentation. For Rapid Eye imagery, a ship detection algorithm has been developed. Finally, some applications are described: Ship monitoring in the Strait of Dover based on TerraSAR-X StripMap using AIS information for verification, analyzing TerraSAR-X HighResolution scenes of an industrial harbor and finally an example of surveying a wind farm using change detection
The project DEKO (Detection of artificial objects in sea areas) is integrated in the DeMarine-Security project and focuses on the detection and classification of ships and off shore artificial objects relying on TerraSAR-X as well as on RapidEye optical images. The DEKO project has been started in Mai 2008. The main expected outcomes of the DEKO project are 1/ the definition of concepts for GMES downstream services based on the obtained results, 2/ the development of new detection and classification algorithms for the analysis of ships and off shore artificial objects and 3/ the validation of the results w.r.t. the sensor acquisition parameters. This paper presents preliminary results obtained in the early stage of the DEKO project like the state of the art on ship detection and classification in SAR images, the currently implemented detection and classification algorithms as well as the first results obtained for ship detection in TerraSAR-X images
For surveillance and reconnaissance tasks small UAVs are of growing importance. These UAVs have an endurance of several hours, but a small payload of about some kilograms. As a consequence lightweight sensors and cameras have to be used without having a mechanical stabilized high precision sensor-platform, which would exceed the payload and cost limitations. An example of such a system is the German UAV Luna with optical and IR sensors on board. For such platforms we developed image exploitation algorithms. The algorithms comprise mosaiking, stabilization, image enhancement, video based moving target indication, and stereo-image generation. Other products are large geo-coded image mosaics, stereo mosaics, and 3-D-model generation. For test and assessment of these algorithms the experimental system ABUL has been developed, in which the algorithms are integrated. The ABUL system is used for tests and assessment by military PIs
It is expected, that ship detection and classification in SAR satellite imagery will be part of future downstream services for various applications, e.g. surveillance of fishery zones or tracking of cargo ships. Due to the requirements of operational services and due to the potential of high resolution SAR (e.g. TerraSAR-X), there is a need for composing, optimization, and validation of specific fully automated image processing chains. The presented processing chain covers all steps from land masking, screening, object segmentation, feature extraction to classification and parameter estimation. The chain is base for experiments with both open sea and harbor scenes for ship detection and monitoring. Within this chain, a classification component for SAR ship and non-ship decision is investigated. Based on many extracted image features and numerous image chips for training and test, some promissing results are presented and discussed. Since the classification can reduce the false alarms of the screening component, the processing chain is expected to work on images with less good weather and signal conditions and to extract ships with lower reflexions
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