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10 November 2003 Characterizing low-signature targets in background using spatial and spectral features
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Developments in the area of signature suppression make it progressively more difficult to recognize targets. Due to the high resolution of modern sensors it is necessary to focus on a wide range of target and partial target sizes, i.e. small structures as well as the whole target. Measures of the difference between targets and background are crucial when assessing signature reduction efforts. These measures should to some extent be associated with the process of detection of targets in background. Two approaches are feasible, trying to simulate human performance or using an autonomous sensor. In both cases we have to rely on a set of features discriminating targets from the background. In the spatial domain we need filters on different scales. The smallest filter will not be able to catch statistical features but has to be based on the use of small image parts like blobs and lines. Larger filter will give statistically relevant feature values. In addition, spectral properties can be used in a multi-dimensional approach investigating targets on different scales, i.e. from very low-resolution to well-resolved objects. Experiments with a new set of features and the use of linear discriminant analysis to get overall signature assessment values are described.
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Sten Nyberg and Lars Bohman "Characterizing low-signature targets in background using spatial and spectral features", Proc. SPIE 5152, Infrared Spaceborne Remote Sensing XI, (10 November 2003);


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