Characterizing low-signature targets in background using spatial and spectral features

Abstract

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 take into account small structures as well as the whole target. Measures describing the difference between targets and background are crucial when assessing signature reduction efforts. These measures should 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 wellresolved objects. Experiments with a new set of featuresand the use of linear discriminant analysis to get overall signature assessment values are described.

Search by photo methodology for signature properties assessment by human observers

“…Photo-simulation by using human observers as an assessment method of camouflage effectiveness has been used in various forms in the recent decades [1][2][3][4]. Other methodologies have also been tested out, involving video surveillance [5], simulation of human vision [6][7][8][9][10][11], similarity measures of target-background by image analysis techniques [2,11], assessment by simulation of target vehicles against different backgrounds [12], and image sequences taken by approaching sensors [13].…”